{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f02249e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libaries\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c6ddce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LEMCell(nn.Module):\n",
    "    def __init__(self, ninp, nhid, dt):\n",
    "        super(LEMCell, self).__init__()\n",
    "        self.ninp = ninp\n",
    "        self.nhid = nhid\n",
    "        self.dt = dt\n",
    "        self.inp2hid = nn.Linear(ninp, 4 * nhid)\n",
    "        self.hid2hid = nn.Linear(nhid, 3 * nhid)\n",
    "        self.transform_z = nn.Linear(nhid, nhid)\n",
    "        self.reset_parameters()\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        std = 1.0 / np.sqrt(self.nhid)\n",
    "        for w in self.parameters():\n",
    "            w.data.uniform_(-std, std)\n",
    "\n",
    "    def forward(self, x, y, z):\n",
    "        transformed_inp = self.inp2hid(x)\n",
    "        transformed_hid = self.hid2hid(y)\n",
    "        i_dt1, i_dt2, i_z, i_y = transformed_inp.chunk(4, 1)\n",
    "        h_dt1, h_dt2, h_y = transformed_hid.chunk(3, 1)\n",
    "\n",
    "        ms_dt_bar = self.dt * torch.sigmoid(i_dt1 + h_dt1)\n",
    "        ms_dt = self.dt * torch.sigmoid(i_dt2 + h_dt2)\n",
    "\n",
    "        z = (1. - ms_dt) * z + ms_dt * torch.tanh(i_y + h_y)\n",
    "        y = (1. - ms_dt_bar) * y + ms_dt_bar * torch.tanh(self.transform_z(z) + i_z)\n",
    "\n",
    "        return y, z\n",
    "\n",
    "# Define the LEM model\n",
    "class LEM(nn.Module):\n",
    "    def __init__(self, ninp, nhid, nout, dt=1.):\n",
    "        super(LEM, self).__init__()\n",
    "        self.nhid = nhid\n",
    "        self.cell = LEMCell(ninp, nhid, dt)\n",
    "        self.classifier = nn.Linear(nhid, nout)\n",
    "        self.init_weights()\n",
    "\n",
    "    def init_weights(self):\n",
    "        for name, param in self.named_parameters():\n",
    "            if 'classifier' in name and 'weight' in name:\n",
    "                nn.init.kaiming_normal_(param.data)\n",
    "\n",
    "    def forward(self, input):\n",
    "        y = input.data.new(input.size(1), self.nhid).zero_()\n",
    "        z = input.data.new(input.size(1), self.nhid).zero_()\n",
    "        for x in input:\n",
    "            y, z = self.cell(x, y, z)\n",
    "        out = self.classifier(y)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36d6fa55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Toy problem data\n",
    "input_size = 256  # number of columns in a dataset\n",
    "hidden_size = 32  # number of neurons\n",
    "output_size = 256  \n",
    "sequence_length = 160  # number of sequences/ number of rows\n",
    "batch_size = 1\n",
    "num_epochs = 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "519594c6",
   "metadata": {},
   "source": [
    "### importing PINN solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2103e0ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the .mat file\n",
    "v_data = scipy.io.loadmat('v.mat')\n",
    "h_data = scipy.io.loadmat('h.mat')\n",
    "x_data = scipy.io.loadmat('x.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "172f53d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x_data['X']\n",
    "u = h_data['h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf954b1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f871c0527b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdabefdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data shape (256,)\n",
      "input data shape (160, 256)\n",
      "Target data shape (160, 256)\n"
     ]
    }
   ],
   "source": [
    "input_data = u[0:160,:]\n",
    "target_data = u[1:161, :]\n",
    "\n",
    "test_data = u[160, :]\n",
    "#test_target = u[161:201, :]\n",
    "\n",
    "print(\"test data shape\", test_data.shape)\n",
    "#print(\"test target shape\", test_target.shape)\n",
    "\n",
    "print(\"input data shape\",input_data.shape)\n",
    "print(\"Target data shape\",target_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b32d98b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input tensor shape torch.Size([1, 160, 256])\n",
      "Target tensor shape torch.Size([1, 160, 256])\n"
     ]
    }
   ],
   "source": [
    "# Convert data to tensors\n",
    "input_tensor = torch.tensor(input_data).view(batch_size, sequence_length, input_size).float()\n",
    "target_tensor = torch.tensor(target_data).view(batch_size, sequence_length, output_size).float()\n",
    "\n",
    "print(\"input tensor shape\",input_tensor.shape)\n",
    "print(\"Target tensor shape\",target_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4c0af1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert test data to tensors\n",
    "test_tensor = torch.tensor(test_data).view(batch_size, 1, input_size).float()\n",
    "#test_target_tensor = torch.tensor(test_target).view(batch_size, 40, output_size).float()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "928ae47f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10/2000, Loss: 0.478989\n",
      "Epoch: 20/2000, Loss: 0.306083\n",
      "Epoch: 30/2000, Loss: 0.197181\n",
      "Epoch: 40/2000, Loss: 0.131788\n",
      "Epoch: 50/2000, Loss: 0.088712\n",
      "Epoch: 60/2000, Loss: 0.062530\n",
      "Epoch: 70/2000, Loss: 0.047468\n",
      "Epoch: 80/2000, Loss: 0.039097\n",
      "Epoch: 90/2000, Loss: 0.034614\n",
      "Epoch: 100/2000, Loss: 0.032332\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/localhome/tkapoor/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([1, 160, 256])) that is different to the input size (torch.Size([160, 256])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 110/2000, Loss: 0.031220\n",
      "Epoch: 120/2000, Loss: 0.030707\n",
      "Epoch: 130/2000, Loss: 0.030486\n",
      "Epoch: 140/2000, Loss: 0.030397\n",
      "Epoch: 150/2000, Loss: 0.030363\n",
      "Epoch: 160/2000, Loss: 0.030350\n",
      "Epoch: 170/2000, Loss: 0.030345\n",
      "Epoch: 180/2000, Loss: 0.030340\n",
      "Epoch: 190/2000, Loss: 0.030325\n",
      "Epoch: 200/2000, Loss: 0.030225\n",
      "Epoch: 210/2000, Loss: 0.029811\n",
      "Epoch: 220/2000, Loss: 0.028870\n",
      "Epoch: 230/2000, Loss: 0.027680\n",
      "Epoch: 240/2000, Loss: 0.026482\n",
      "Epoch: 250/2000, Loss: 0.025352\n",
      "Epoch: 260/2000, Loss: 0.024305\n",
      "Epoch: 270/2000, Loss: 0.023341\n",
      "Epoch: 280/2000, Loss: 0.022450\n",
      "Epoch: 290/2000, Loss: 0.021624\n",
      "Epoch: 300/2000, Loss: 0.020855\n",
      "Epoch: 310/2000, Loss: 0.020137\n",
      "Epoch: 320/2000, Loss: 0.019463\n",
      "Epoch: 330/2000, Loss: 0.018830\n",
      "Epoch: 340/2000, Loss: 0.018234\n",
      "Epoch: 350/2000, Loss: 0.017670\n",
      "Epoch: 360/2000, Loss: 0.017136\n",
      "Epoch: 370/2000, Loss: 0.016629\n",
      "Epoch: 380/2000, Loss: 0.016148\n",
      "Epoch: 390/2000, Loss: 0.015690\n",
      "Epoch: 400/2000, Loss: 0.015253\n",
      "Epoch: 410/2000, Loss: 0.014836\n",
      "Epoch: 420/2000, Loss: 0.014436\n",
      "Epoch: 430/2000, Loss: 0.013979\n",
      "Epoch: 440/2000, Loss: 0.012540\n",
      "Epoch: 450/2000, Loss: 0.011016\n",
      "Epoch: 460/2000, Loss: 0.009700\n",
      "Epoch: 470/2000, Loss: 0.008641\n",
      "Epoch: 480/2000, Loss: 0.007763\n",
      "Epoch: 490/2000, Loss: 0.007041\n",
      "Epoch: 500/2000, Loss: 0.006435\n",
      "Epoch: 510/2000, Loss: 0.005919\n",
      "Epoch: 520/2000, Loss: 0.005473\n",
      "Epoch: 530/2000, Loss: 0.005083\n",
      "Epoch: 540/2000, Loss: 0.004739\n",
      "Epoch: 550/2000, Loss: 0.004432\n",
      "Epoch: 560/2000, Loss: 0.004155\n",
      "Epoch: 570/2000, Loss: 0.003905\n",
      "Epoch: 580/2000, Loss: 0.003676\n",
      "Epoch: 590/2000, Loss: 0.003467\n",
      "Epoch: 600/2000, Loss: 0.003274\n",
      "Epoch: 610/2000, Loss: 0.003096\n",
      "Epoch: 620/2000, Loss: 0.002933\n",
      "Epoch: 630/2000, Loss: 0.002784\n",
      "Epoch: 640/2000, Loss: 0.002647\n",
      "Epoch: 650/2000, Loss: 0.002521\n",
      "Epoch: 660/2000, Loss: 0.002396\n",
      "Epoch: 670/2000, Loss: 0.002284\n",
      "Epoch: 680/2000, Loss: 0.002179\n",
      "Epoch: 690/2000, Loss: 0.002081\n",
      "Epoch: 700/2000, Loss: 0.001989\n",
      "Epoch: 710/2000, Loss: 0.001904\n",
      "Epoch: 720/2000, Loss: 0.001823\n",
      "Epoch: 730/2000, Loss: 0.001748\n",
      "Epoch: 740/2000, Loss: 0.001676\n",
      "Epoch: 750/2000, Loss: 0.001609\n",
      "Epoch: 760/2000, Loss: 0.001546\n",
      "Epoch: 770/2000, Loss: 0.001489\n",
      "Epoch: 780/2000, Loss: 0.001431\n",
      "Epoch: 790/2000, Loss: 0.001378\n",
      "Epoch: 800/2000, Loss: 0.001328\n",
      "Epoch: 810/2000, Loss: 0.001281\n",
      "Epoch: 820/2000, Loss: 0.001236\n",
      "Epoch: 830/2000, Loss: 0.001193\n",
      "Epoch: 840/2000, Loss: 0.001152\n",
      "Epoch: 850/2000, Loss: 0.001114\n",
      "Epoch: 860/2000, Loss: 0.001077\n",
      "Epoch: 870/2000, Loss: 0.001043\n",
      "Epoch: 880/2000, Loss: 0.001031\n",
      "Epoch: 890/2000, Loss: 0.000979\n",
      "Epoch: 900/2000, Loss: 0.000950\n",
      "Epoch: 910/2000, Loss: 0.000921\n",
      "Epoch: 920/2000, Loss: 0.000892\n",
      "Epoch: 930/2000, Loss: 0.000867\n",
      "Epoch: 940/2000, Loss: 0.000841\n",
      "Epoch: 950/2000, Loss: 0.000818\n",
      "Epoch: 960/2000, Loss: 0.000810\n",
      "Epoch: 970/2000, Loss: 0.000788\n",
      "Epoch: 980/2000, Loss: 0.000753\n",
      "Epoch: 990/2000, Loss: 0.000733\n",
      "Epoch: 1000/2000, Loss: 0.000714\n",
      "Epoch: 1010/2000, Loss: 0.000696\n",
      "Epoch: 1020/2000, Loss: 0.000678\n",
      "Epoch: 1030/2000, Loss: 0.000661\n",
      "Epoch: 1040/2000, Loss: 0.000648\n",
      "Epoch: 1050/2000, Loss: 0.000635\n",
      "Epoch: 1060/2000, Loss: 0.000624\n",
      "Epoch: 1070/2000, Loss: 0.000603\n",
      "Epoch: 1080/2000, Loss: 0.000588\n",
      "Epoch: 1090/2000, Loss: 0.000575\n",
      "Epoch: 1100/2000, Loss: 0.000561\n",
      "Epoch: 1110/2000, Loss: 0.000556\n",
      "Epoch: 1120/2000, Loss: 0.000526\n",
      "Epoch: 1130/2000, Loss: 0.000533\n",
      "Epoch: 1140/2000, Loss: 0.000519\n",
      "Epoch: 1150/2000, Loss: 0.000507\n",
      "Epoch: 1160/2000, Loss: 0.000495\n",
      "Epoch: 1170/2000, Loss: 0.000485\n",
      "Epoch: 1180/2000, Loss: 0.000476\n",
      "Epoch: 1190/2000, Loss: 0.000472\n",
      "Epoch: 1200/2000, Loss: 0.000459\n",
      "Epoch: 1210/2000, Loss: 0.000458\n",
      "Epoch: 1220/2000, Loss: 0.000445\n",
      "Epoch: 1230/2000, Loss: 0.000435\n",
      "Epoch: 1240/2000, Loss: 0.000426\n",
      "Epoch: 1250/2000, Loss: 0.000419\n",
      "Epoch: 1260/2000, Loss: 0.000420\n",
      "Epoch: 1270/2000, Loss: 0.000404\n",
      "Epoch: 1280/2000, Loss: 0.000405\n",
      "Epoch: 1290/2000, Loss: 0.000392\n",
      "Epoch: 1300/2000, Loss: 0.000385\n",
      "Epoch: 1310/2000, Loss: 0.000380\n",
      "Epoch: 1320/2000, Loss: 0.000395\n",
      "Epoch: 1330/2000, Loss: 0.000376\n",
      "Epoch: 1340/2000, Loss: 0.000366\n",
      "Epoch: 1350/2000, Loss: 0.000359\n",
      "Epoch: 1360/2000, Loss: 0.000351\n",
      "Epoch: 1370/2000, Loss: 0.000358\n",
      "Epoch: 1380/2000, Loss: 0.000343\n",
      "Epoch: 1390/2000, Loss: 0.000342\n",
      "Epoch: 1400/2000, Loss: 0.000333\n",
      "Epoch: 1410/2000, Loss: 0.000329\n",
      "Epoch: 1420/2000, Loss: 0.000323\n",
      "Epoch: 1430/2000, Loss: 0.000319\n",
      "Epoch: 1440/2000, Loss: 0.000329\n",
      "Epoch: 1450/2000, Loss: 0.000310\n",
      "Epoch: 1460/2000, Loss: 0.000309\n",
      "Epoch: 1470/2000, Loss: 0.000303\n",
      "Epoch: 1480/2000, Loss: 0.000301\n",
      "Epoch: 1490/2000, Loss: 0.000305\n",
      "Epoch: 1500/2000, Loss: 0.000293\n",
      "Epoch: 1510/2000, Loss: 0.000288\n",
      "Epoch: 1520/2000, Loss: 0.000278\n",
      "Epoch: 1530/2000, Loss: 0.000347\n",
      "Epoch: 1540/2000, Loss: 0.000265\n",
      "Epoch: 1550/2000, Loss: 0.000260\n",
      "Epoch: 1560/2000, Loss: 0.000257\n",
      "Epoch: 1570/2000, Loss: 0.000250\n",
      "Epoch: 1580/2000, Loss: 0.000245\n",
      "Epoch: 1590/2000, Loss: 0.000241\n",
      "Epoch: 1600/2000, Loss: 0.000237\n",
      "Epoch: 1610/2000, Loss: 0.000233\n",
      "Epoch: 1620/2000, Loss: 0.000236\n",
      "Epoch: 1630/2000, Loss: 0.000226\n",
      "Epoch: 1640/2000, Loss: 0.000222\n",
      "Epoch: 1650/2000, Loss: 0.000218\n",
      "Epoch: 1660/2000, Loss: 0.000215\n",
      "Epoch: 1670/2000, Loss: 0.000211\n",
      "Epoch: 1680/2000, Loss: 0.000207\n",
      "Epoch: 1690/2000, Loss: 0.000203\n",
      "Epoch: 1700/2000, Loss: 0.000199\n",
      "Epoch: 1710/2000, Loss: 0.000196\n",
      "Epoch: 1720/2000, Loss: 0.000199\n",
      "Epoch: 1730/2000, Loss: 0.000201\n",
      "Epoch: 1740/2000, Loss: 0.000192\n",
      "Epoch: 1750/2000, Loss: 0.000185\n",
      "Epoch: 1760/2000, Loss: 0.000183\n",
      "Epoch: 1770/2000, Loss: 0.000178\n",
      "Epoch: 1780/2000, Loss: 0.000175\n",
      "Epoch: 1790/2000, Loss: 0.000172\n",
      "Epoch: 1800/2000, Loss: 0.000169\n",
      "Epoch: 1810/2000, Loss: 0.000166\n",
      "Epoch: 1820/2000, Loss: 0.000165\n",
      "Epoch: 1830/2000, Loss: 0.000278\n",
      "Epoch: 1840/2000, Loss: 0.000168\n",
      "Epoch: 1850/2000, Loss: 0.000170\n",
      "Epoch: 1860/2000, Loss: 0.000161\n",
      "Epoch: 1870/2000, Loss: 0.000154\n",
      "Epoch: 1880/2000, Loss: 0.000151\n",
      "Epoch: 1890/2000, Loss: 0.000148\n",
      "Epoch: 1900/2000, Loss: 0.000146\n",
      "Epoch: 1910/2000, Loss: 0.000145\n",
      "Epoch: 1920/2000, Loss: 0.000143\n",
      "Epoch: 1930/2000, Loss: 0.000141\n",
      "Epoch: 1940/2000, Loss: 0.000145\n",
      "Epoch: 1950/2000, Loss: 0.000160\n",
      "Epoch: 1960/2000, Loss: 0.000142\n",
      "Epoch: 1970/2000, Loss: 0.000135\n",
      "Epoch: 1980/2000, Loss: 0.000134\n",
      "Epoch: 1990/2000, Loss: 0.000133\n",
      "Epoch: 2000/2000, Loss: 0.000130\n"
     ]
    }
   ],
   "source": [
    "# # Create LEM instance\n",
    "# lem = LEM(input_size, hidden_size, output_size, dt=0.01)\n",
    "\n",
    "# # Loss and optimizer\n",
    "# criterion = nn.MSELoss()\n",
    "# optimizer = torch.optim.Adam(lem.parameters(), lr=0.1)\n",
    "# Create LEM instance :: niche h RNN vala \n",
    "lem = LEM(input_size, hidden_size, output_size, dt=0.1)\n",
    "\n",
    "# Loss and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(lem.parameters(), lr=0.01)\n",
    "\n",
    "\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    output = lem(input_tensor)\n",
    "    loss = criterion(output, target_tensor)\n",
    "\n",
    "    # Backward and optimize\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # Print progress\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f'Epoch: {epoch + 1}/{num_epochs}, Loss: {loss.item():.6f}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3799af",
   "metadata": {},
   "source": [
    "### Exact solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "86371afd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 256)\n",
      "(1, 201)\n",
      "(201, 256)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/localhome/tkapoor/.local/lib/python3.8/site-packages/numpy/ma/core.py:2820: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n",
      "/usr/local/lib/python3.8/dist-packages/matplotlib/contour.py:1180: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  self.levels = np.asarray(levels_arg).astype(np.float64)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzMAAAFNCAYAAAA0HDEuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABj9ElEQVR4nO29ebwsV1mv/7zd+5wTMSEgiUwJBGVQHNGAKHIZgswSFUFQkCARUVAvFxliuMAPr9coXgUvKB4RA4oMIkiUKASFC8pgoqJMgpExIRpCgICQ5Oze7++PqupdXbuGVVNXVff3+Xz2Z3fNq6uqV62n3jWYuyOEEEIIIYQQU2M2dAKEEEIIIYQQogmSGSGEEEIIIcQkkcwIIYQQQgghJolkRgghhBBCCDFJJDNCCCGEEEKISSKZEUIIIYQQQkwSyYwQQsSY2cfN7D4DHfssM/vb1PSXzOzrhkjLWDGzHzOzNw+dDiGEEONBMiOE2EjM7HvN7J1m9gUzu9rM/s7M7jx0ukJx9+Pd/aNDpyOWrEUsV+m/W/R83NPMzM1sJ5nn7q9w9/v2eVwhhBDTYqd6FSGEmBZmdkPgL4CfBl4DHAbuDlzX4TF23H23q/2NgZLv9C53/961J0gIIYSoQJEZIcQmcnsAd3+luy/c/Svu/mZ3/xcAM/tJM/uQmX3RzD5oZt+R2vbbzexf4ojOq83suHibe5rZZWb2dDP7D+APzOyImT3fzD4d/z3fzI5k1n+KmV1pZleY2WOTg5jZTczsAjO7xsz+Hvj69BeIoxK3jT+fb2YvMrM3xml+j5l9fWrd+5rZh+M0/7aZ/T8zOzu1/Cfi7/s5M3uTmd06c5wnmtm/Af9W90Sb2Z3M7B/jdL3azF5lZv8rXrZSdS7nez3IzP4pPgefMrPnpFZ9e/z/83Ek6LtzquJ9j5ldHH/vi83se1LL3mZmvxRH5L5oZm82s5Pqfj8hhBDjRjIjhNhEPgIszOxlZvYAM7txssDMHgY8B/hx4IbAQ4DPprZ9OHB/4DbAtwJnpZbdDPga4NbA44FzgbsC3w58G3AX4JmZ9U8Ebgk8DnhRKi0vAq4Fbg78RPxXxiOA/w+4MXAp8Mvx9zkJeC1wDnAT4MNAulB/JvCLwA8BJwPvAF6Z2fcPAN8F3LEiDSuY2WHgz4A/JDovfwI8tMYu/ovoOtwIeBDw02b2A/Gy/xb/v1Fc5e5dmWN/DfBG4LeIvvdvAG80s5ukVvtR4LHA1xJF536hRtqEEEJMAMmMEGLjcPdrgO8FHPg94DNxFOSmwNnAr7n7xR5xqbt/IrX5b7n7p939auDPiUQlYQ94trtf5+5fAX4MeK67X+nunyGSjUen1j8WLz/m7hcCXwLuYGZzokL/s9z9v9z9/cDLKr7W69397+NqYK9IpeuBwAfc/XXxst8C/iO13ROAX3H3D8XL/zdR9OnWqXV+xd2vjr9THnc1s8+n/v49mQ8cAp4ff8fXAhdXfI8l7v42d3+fu+/FUbNXAvcI3PxBwL+5+x+6+667vxL4V+D7U+v8gbt/JP5er2H1WgohhNgAJDNCiI0kLryf5e6nAN8M3AJ4PnAq8O8lm6ZF4MvA8anpz7j7tanpWwBpEfpEPC/hs5k2KMn+TiZqs/ipzLZlFKXrFun9uLsDl6XWvTXwgkREgKsBI4oWJXwKwMzunmrg/4HU8ne7+41Sf0kVt1sAl8fHDP0eS8zsu8zsrWb2GTP7ApF4hVYFy5775Njp71V2LYUQQmwAkhkhxMbj7v8KnE8kNZ8i0z6lzq4y058mkoWEW8XzqvgMsEskVultm3AFcEoyYWaWnib6vj+VkZGvcvd3ptZxAHd/R1yl63h3/6bAY98yPmbe9/gv4AaptN0ss/0fAxcAp7r7icCLiURrmaYSsuc+OfblAekWQgixIUhmhBAbh5l9Q9zw/pR4+lTgkcC7gZcAv2Bm32kRt81UuarDK4FnmtnJcduVZwF/VLWRuy+A1wHPMbMbmNkdgcc0TMMbgW8xsx+wqBvjJxK11Ul4MXCOmX0TgJmdGLcb6oJ3EUnZz5nZITP7IaJ2Qwn/DHyTmX27RR0pPCez/QnA1e5+rZndhaiNS8JniKr1FY21cyFwezP7UTPbMbMfIWrz8xetv5UQQojJIJkRQmwiXyRq0P4eM/svIol5P/AUd/8Tosbzfxyv92dEjdeb8L+AS4B/Ad4H/GM8L4QnEVV7+g+iqNEfNEmAu18FPAz4NaKODO4Yp+m6ePnrgV8FXmVm1xCdhwfUPMx328FxZu7s7tcTdSxwFlH1tR8hkrQkbR8Bngu8haintL/N7PdngOea2ReJRPA1qW2/THSd/i6uInfXzPf+LPBg4Cnx934a8OD4fAghhNgSbLWqsxBCiCljZjOiNjM/5u5vHeD45wOXufszq9YVQggh2qLIjBBCTBwzu5+Z3ciiMW5+kajdybsHTpYQQgjRO5IZIYSYPt9N1EPbVURdE/9ASTfLQgghxMagamZCCCGEEEKISaLIjBBCCCGEEGKSSGaEEEIIIYQQk2Rn6ATU4aQTT/Bb3ezkoZNRA1XhE0IIMXWsehUhNpB/+sjHrnL3URc8v3P+1X6NL2pvd6lf9yZ3v38PSVo7k5KZW93sZP7ud3956GQcZG936BQIIYQQ/TKbVJFBiNbc4F4/+omh01DFNb7gBcedVnu7B33lwyd1n5phUM5UhURFCCGEqPc8lPgIIdbExHIbz89M62SakhMhhBCiX7p41kqIhKjEZsb8q+b1N9ygzvs3I6eQoAghhBCbRdmzXaIjhIhRbiCEEEKIaaEqb0IAYHPj0AkNIjNXd5+WodAvXAghhBCbS5X4SHaEmDT6BQshhBBie2lSVT1PgJpWec/uK70fiZYQlehXIoQQQghRhy7b6pbtq+s2wZKjjcNmsHN8g2pmG4TuaiGEEEKIbaCrKJQQI0J3qBBCCCGEyCdEgCQ8YkB09wkhhBBCiOa0HQNQNMeM+aHZ0KkYFN1pQgghhBCiW+pWaZP8iIZst8oJIYQQQojh2dtt/ic6x8xeamZXmtn7C5afaGZ/bmb/bGYfMLPHxvO/3czeFc/7FzP7kdQ2Z5jZP5rZe83sb83stl2kVRoshBBCCCGmyxYLjc1g57heivPnAy8EXl6w/InAB939+83sZODDZvYK4MvAj7v7v5nZLYB/MLM3ufvngd8BznT3D5nZzwDPBM5qm1DJjBBCCCGEEGKJu7/dzE4rWwU4wcwMOB64Gth194+k9vFpM7sSOBn4fLzNDePFJwKf7iKtkhkhhBBCCCEmiJkxP9yo1chJZnZJavqoux+tsf0LgQuIhOQE4EfcfS+TtrsAh4F/j2edDVxoZl8BrgHu2iThWSQzQgghhBBCbBdXufvpLba/H/Be4N7A1wMXmdk73P0aADO7OfCHwGNSkvNk4IHu/h4zeyrwG0SC0wp1ACCEEEIIIYSow2OB13nEpcDHgG8AMLMbAm8EznX3d8fzTga+zd3fE2//auB7ukiIIjNCCCGEEEJMETN2jgxSnP8kcAbwDjO7KXAH4KNmdhh4PfByd39tav3PASea2e3jdjXfB3yoi4RIZoQQQgghhBBLzOyVwD2J2tZcBjwbOATg7i8Gfgk438zeBxjwdHe/ysweBfw34CZmdla8u7Pc/b1m9pPAn5rZHpHc/EQXaZXMCCGEEEIIIZa4+yMrln8auG/O/D8C/qhgm9cTRW06ZXCZMbM5cAlwubs/eOj0CCGEEEIIMQXMYLYzHzoZgzKGDgB+no7qzAkhhBBCCCG2h0FlxsxOAR4EvGTIdAghhBBCCCGmx9DVzJ4PPI1osB0hhBBCCCFEIDYzdo6omtkgmNmDgSvd/R8q1nu8mV1iZpdc9fkvril1QgghhBBCiLEzZGTmbsBDzOyBwHHADc3sj9z9UemV3P0ocBTgO+5wG19/MoUQQgghhBghZswPD13RalgGi8y4+znufoq7nwY8AvibrMgIIYQQQgghRBFj6M1MCCGEEEIIIWoziriUu78NeNvAyRBCCCGEEGIyaJwZRWaEEEIIIYQQE2UUkZlQzB3b3a29ne/sf82i7dPrlK0Xsm2XNPm+Q9DnORBCCCGEECKPrSiBhghBG2mYinD0ybafA8mcEEIIIdaOGTtHtrsMst3fXoiO2GaZk8gJIYQQYihUChFCtGKbRS4EyZ4QQgjRH3rKCiFEj2yD7EnYhBBiGKLezLa7Py89gYQQQrRi04VNsiaEEONFObQQQghRwqbLWhqJmxATw2bMDx8aOhWDolxLCCGEEMDw4iaZEkLURbmGEEIIIUbB0DLVF5I0MTXM7KXAg4Er3f2bc5b/GPB0wIAvAj/t7v8cL7s/8AJgDrzE3c+L598b+HXgMPAPwOPcvfWPXr8uIYQQQogeGZukSa42BzOYHerlep4PvBB4ecHyjwH3cPfPmdkDgKPAd5nZHHgR8H3AZcDFZnYB8K/Ay4Az3P0jZvZc4DHA77dNqO5mIYQQQogtYki5kkhNA3d/u5mdVrL8nanJdwOnxJ/vAlzq7h8FMLNXAWcCnwGud/ePxOtdBJyDZEYIIYQQQkyFdYqUxGltPA74y/jzLYFPpZZdBnwXcBWwY2anu/slwA8Dp3ZxcF1lIYQQQgixcYytel8vmDHbmTfZ8iQzuyQ1fdTdj9Y/vN2LSGa+t2w9d3czewTwm2Z2BHgzsKh7vDwkM0IIIYQQQmwXV7n76W12YGbfCrwEeIC7fzaefTmrEZdT4nm4+7uAu8fb3he4fZvjJ0xPZvY6kbjtYdbI1oUQQgghhMjFzG4FvA54dKodDMDFwO3M7DZEEvMI4Efjbb7W3a+MIzNPB365i7RMT2ZEPSR/Yl1InIUQQoi1YmbMDndfnDezVwL3JKqOdhnwbOAQgLu/GHgWcBPgt80MYNfdT3f3XTN7EvAmoq6ZX+ruH4h3+1QzezAwA37H3f+mi7RKZoQQ3SBxjpDUCSGEmDju/siK5WcDZxcsuxC4MGf+U4GndpLAFJIZIYTokm2WOomcEEKsF4PZfLvzXsmMEEKIbtg2kZO8CSHE4EhmhBBCiCZMVd4kYUKIDUIyI4QQQmwTU5WwUCRrYpvoqQOAKbHd314IIYQQm8UYZE1CJcTakMwIIYQQQnTJGIQqBEmX2AAkM0IIIYQQ28jYpEtyVRszY3bo0NDJGJRpyYw77B4bOhVCrJ+d7c6ohBBCbAFjkCsJ1eSYlswIsa1I4seDxFIIITaXMQiVqIVkRggh6iCxjJDUCSHE8JhhGjRTCCGEqMk2S51ETgghRoNkRgghhKjDNoscSOaEGBFRBwDbXZzf7m8vhBBCiHpMUeYkYEJsLNOSmU3rzUyZqxBCCNE/Uy07qJwgRCXTkplNY6qZq9ADRgghRP9MoZyg5+Hg2I46ABBC1GUKD5hNRw9QIYQYniGfh3oOCCQzQoipIqHsFhUKhBBTQ88BgWRGCCEEqFDQFZJCIcQ6McO2PN+RzAghhBBdse1SuOWFKiHE+pmWzLjDYjF0KkQRWz4CrRBCbD3bIHMSNrElmNn9gRcAc+Al7n5eZvlZwPOAy+NZL3T3l8TLfg14EDADLgJ+3t3dzH4EODfe51+4+9PbpnNaMiPGjUSzHpI/IYSYHlMVNknYZmLWS29mZjYHXgR8H3AZcLGZXeDuH8ys+mp3f1Jm2+8B7gZ8azzrb4F7mNn7iOTnO939M2b2MjM7w93/uk1aJTNCDIXkT0InhBDrYmgJk0xNjbsAl7r7RwHM7FXAmUBWZvJw4DjgMGDAIeA/ga8D/s3dPxOv9xbgoYBkRggxUSR03SExFEKMmXXL1LbIkxnsNCrOn2Rml6Smj7r70dT0LYFPpaYvA74rZz8PNbP/BnwEeLK7f8rd32VmbwWuIJKZF7r7h8zsxsAdzOy0eH8/QCQ8rZDMCCHEJiAxnC4SUSG6Z+hI1Pi5yt1Pb7mPPwde6e7XmdlPAS8D7m1mtwW+ETglXu8iM7u7u7/DzH4aeDWwB7wT+PqWaZiazLhuzk1nW96kCCFEgkS0XySLQjThcuDU1PQp7Df0B8DdP5uafAnwa/HnHwTe7e5fAjCzvwS+G3iHu/85kQRhZo8HWmeAE5MZsfFso6xK4IQQoj82TRYlZyKNGXaol3LExcDtzOw2RBLzCOBHVw9tN3f3K+LJhwAfij9/EvhJM/sVompm9wCeH2/zte5+ZVzl7GeAh7dNqGRGiKGZosBJwIQQYhjGImeSqo3G3XfN7EnAm4i6UX6pu3/AzJ4LXOLuFwA/Z2YPAXaBq4Gz4s1fC9wbeB9RZwB/FUdkAF5gZt8Wf36uu3+kbVolM0KI+kxRwEKQpAkhRBhDSJUEaq24+4XAhZl5z0p9Pgc4J2e7BfBTBft8ZMfJHE5mzOxU4OXATYms7ai7v6BsG3fwsbyRELUwZUBiCmyqpInmSHCFGA/rKANOrLxipjLWkJGZXeAp7v6PZnYC8A9mdlHOYDxiA9gECd32zEKIrUSCK6qQ8G4WG1Be2TYGk5m4wdAV8ecvmtmHiPq0lsyIUTIFIZNwCSHEmpma8Eq+xIYxijYz8eA5dwLeU7qiO35sYplGT/TUc4WYOGMSLomVEEKMkDHIl4SqQ2zrz+fgMmNmxwN/Cvx3d78mZ/njgccDnHqTE9ecuvGyyVInUdsMxiRWWSRaQggxIOsQqi0v4G8Tg8qMmR0iEplXuPvr8tZx96PAUYDvuM0tfY3JEwMxNlGTXG0eYxYtIcR00IuRETOGCNQ6MJtcpwVdM2RvZgb8PvAhd/+NoI3cYXe313T1zs7gwTBRkzHIlYRKCCHGxya8GJGQiakzZMn6bsCjgfeZ2Xvjeb8Y92m9uUxdxoqQpPXKuoVK8iSEENvB0EImmRJtGbI3s78FbKjji44Zk6RJrFqzLnmSNAkhxHbTt0xtvCyZOgCYVqnPHd+dfkg3i+1s+A9t3axLrCRNrVHESWwjY6i6KqaP8rMwho48if5RaWwETFHQJGD0K00SpV4YayFymwolY70GQkyNsf6Wtik/E+NAJSbRiCEEbKsEqmtRkhyNmrEWSoQQoi6qprxmVM1sYjLjjm9LV3sB2JbdvH0K1MaLUpdyJDESYvsYU7vIbUV57wpdSpPEaNrolzFhxiB2myJUXYjSxgtRQttCjR7IQuwjSRChjP1emXDeruj4tJncnTf29iVbU6CN6VOopiZKbe/Nrbl3JENizIy9wCjEWFnHb0f5fz6zLSk/FKC7omMkW93RpShNQYya3DtTup6dIRnabiQbk2Psz8VNZSOfD6oyLXKY1JV0d/aOhd/Is0OT+nprYR0PlTFmoG3EaMwiVOd6jvG6DEKbh6EeftVINkaBBEKM5R4Y7bNnQ/IqN8O3/Nm00d++jvhMjTGLWlcZ6FgywLoiNFb5Cb0uYznvo2Soh1/Rg2pDHsbbzFgKnEL0RV/3uJ5VImG8JWJRyjpEbWhhapoBDp3BhcrPVKVn6PO7lUhaRotkpJoxdFYj2jHG55XaqQ6HmX0N8GrgNODjwMPd/XM56z0GeGY8+b/c/WXx/L8Cbk7kIe8Anujui9D9ZpmWzLhvxUiuNh/HD6wrYVq3FNXN4IbK0Koe8GN8eEDx+dWDQUydbRMTSYYIZR33yrqfeZvzezeYrb04/wzgr939PDN7Rjz99JVURWLybOB0wIF/MLMLYjl5uLtfY2YGvBZ4GPCqkP3mMS2Z2RL6FrZ1y1JTKVqXBIVkaEMU1MseHmMUnbzzKMERY2BzCi2rSEYiNvX69s3Y8ucu7ucxPhs3lDOBe8afXwa8jYPScT/gIne/GsDMLgLuD7zS3a+J19kBDhPJTuh+DzApmYk6ANiczHs20CBNXclS31I0ps4exlb1Ki/TH2Mmnj5vY3twis1i6gXaTRGTqV+HbWLd12odz4Cmv6MxPj9Hzk3d/Yr4838AN81Z55bAp1LTl8XzADCzNwF3Af6SKDoTut8DTEpmNo2+xaxvWWoiRX0JUJX4DCU76yzAZzPxsWXOvruQ0IhOmFKBeSqSMqVz2iXqKGh9jLlzoKn8TnMxw+eNrvVJZnZJavqoux/d3629BbhZznbnpifc3c3Mc9Yrxd3vZ2bHAa8A7g1c1HS/47rTq3Bn7/rpZzyzw+s57W1lqQ8ZChWgrqWn7IHVZ4Y/ZNWrJHMem9QIUZexF7THWBAa+zlLs8lCMSRDn9e+nq0ak60zrnL304sWuvt9ipaZ2X+a2c3d/QozuzlwZc5ql7NfZQzgFKJqY+ljXGtmbyCqXnYRELLfA0xLZjaEvoSsa0lqIkNdCVCV9HQpO0UZ/joy4nWG3SU1YoqMqVA+BmkZ0/kYurDcJ5vQ2dDQnQmNqRMhjcnWORcAjwHOi/+/IWedNwH/28xuHE/fFzjHzI4HToiFZQd4EFGPZqH7PcC0ZMZhbwMymFlfVa1aSFJXIhQiQF0IT9mDpqsMPC8j7lpwtqVNySZ/N9EfQxfch5KXob73GOVkE6RiKIY6d+usWVFEm2f10PlOfQxfv7ieB7zGzB4HfAJ4OICZnQ48wd3PdverzeyXgIvjbZ4bz7spcIGZHQFmwFuBF5ftt4pJyYx3WM1sXVW98uhayLqQozrnte25qxKetrKTl4H3ITh9iM2mFfo35vtMaXRljUnTmnVJzLoKTUNJyhRFZJM6GcpjHR0PjaGTodB7fmztiqaCu38WOCNn/iXA2anplwIvzazzn8Cd6+y3iq29ikO3velSpprIURsBCjl3bb5f0cOkTSbch+D0ITabIDSjTv+UpKQpdb7jyMXHduZrfUvat8T0+V3WISxDysmmS8a6WNd57LsGRh5NnuljjEbWxgyfb3c18mk92d3ZW+ODbdZjoawLmWolDIGZRFPpKft+TdOdlwl3JThdiU1XdXu7FoK+28sMKjDbICh9EXLuBhaedQlNHyLTR7r7KnytQ1TGJiRDv9QcG52+ZB2gA6KhOhgSw6NSQAnrEKc2wlQ3I26SUVVJTxPZKUp3o/RlMsymcpNkgl1IzdjC1n2IzNrkRaIyPGXXYE2ik9xvU6nL3mU6u5SXPoRlnYIi+eiXMXVOVPe+qvPsX2cHQ2I9TKqk4D6ekGBXBdamwtREgvqoHlYmO3VFJy99tdOTygDbvNkZMjPrShS6lJje5GWEsmIDDWbbBh/qjfeaRSd9H45RbLpIUxfPuC6lpQ9ZGUJKNqHzoCHpu2p67jFrPP+77Hxocm29VM1sWjIzJrqUqiZiVEeC6ohPVaZTK3PJyRDaCM46xWYoqWkrDV0ITOfisiZhmaKEdEXb796LDBVd944kZ2zRmrbpaPNMaVv46kpY+pKUsYnIlCJEnfVU2tE1qFMGCD3Pod+x786HxHBMS2Z8j8X146pzmzA/3PxH0He3g1Xi04XsBGcmLQSnrdiMPaNqKhFtBaYTeelYWMYmJkNXO+j7TWGd891afPLulRaC01W0xnYODdIVc5P8v8390EZcuirE9y0nU5KNvlnHuWj7krN03wF5b1e1TsbWpkuEMymZcYe93b1O9jXbmXWyn4S2klVXhkIegKHCUyY7oaLTpopYOnPrU2ySjKpu3dp1FGSbCEUTiWklLh0IS5+SMrRw9Ekf361pgTjkGtYWno6iOG3FZp1CU1dimlyvpoWzNgXgLkVlHQXxdXYqNBXqVmPvsxpZV50V9dEp0VhwwGeb+/wLYdpXsAVdSVERdWWpjgyFik/Zw7KN6DQRnL7FZiyZUd0qg3Xloq7ANJKXFtLShaysTUp67uFt7dQshDc5z8G9BVXcB8Gyk70Xa8hNU7FJfmN9Sk0dkakrMXUFpklBtI2wdCkoQ4vIWNrw5tGmXW8X5zWknNDl+HZt2u922SmRGIZpXSl3dq9bT+axc6TlwJANZClUgKrEJ0R2ijLhkAywieBkM4s6b2TqhJlDQ8mh0Zk6Bb46D486klFHYGrLS01xaSMrnUjKugVkDNGe0IJjV+empBAfeg0rewsquI8qJadhFbUmYtNXlKYPkakjMHVloom4tIrsdCwoYxaOPun7e1c97+pex7pliAPblzz7i+7hNtEcMS6mJTNrpCtpqiNFIQIUIjxlslMlOnkZYF3BqfNGpg+pGeJtSqjIhMpGqMAEy0vP0tJIVLoofA8pG2uQB6Df75j3kK/7vXLSX3U/FBXSG0lOzehNHbGpE6XpcjycLiWmToGsjrg0iui0OD99Fc7H2g53SNq8EC2j7DkZcm+UlS2aVCNrKjnjw9ibbVhNg5pMSmaiNjPDhZWbdIdcR4pCxKdMeJqKTl3BqfNGJvRtS6jUrDOTCS2gh4hMiHSECEyQvASKSx1hqSUrTQr5ba/ruiM2XR5v51DtKmKtSY7X9LynCwEh5yLz/crup7yCfN69Wig4NeQmtEe0dbalCRGZriQmVF5qCVGDZ3QbUelbRvqukr5Ouqr9UUZZeSL0Ohc9U5u2763bpndsveeJaiYlM7izCMxU5z28ne97TJgq8amSnaJMtyoDqys46QypK7EJlZp1C00ZXUhMlcB0JS8h4hIkLHUK8XWvU1NB6Etk1tmg8nDJsfZ6eLC2EajdY+HXNikUVF2jVFqK7sNsIT9YcALkJiRaExKl6TI6k0eVxHQlMEH7qfE9m8hKW0kZQkLWVQ0+oW51+HV0oNRF+9+q+yXv2Vt0PxaVO7oY124suBl76gBgMwmVnibUFaW24dOEsoyyLFPLy8DqCE7XYlMlNWPIUMoK9lXfs1eBqRCXTqQlRA66Fp8mQtIy8/YRDtiZT8U1b9qtcSJQfcgShEvPYlF+/WN5CJGc7P1fKTclYtNUatoITVlUpkxkquSjSmC6kpc60lJXVrooiK9bNvqm7+9TVK6oey2KyhtN2//WafNbp41vk7a9YhxM6kr5nrN7XfsH786RdoWguqIUIj9tx4LJy9TqCE7om5YQsQmRmjZC0zY6U9X4v6nIlElImcD0JS+l0lIlD10IT8g6NYSkkXzM+svifN7Nvm3RrkDiZfnLXsi+V7evlKNQCQqJ/lQJT5nolEjOctDbKrkpEZuqaE1Z1bOuIzRNRaZMYioFqCL9IeJSR1jqFpC7KsgP3SPa0JQ9i5uc47xyR9P2wHXb/4a2+Q0VHHUAMB0mJTNdsW4hCpGfKuGpG0KFgxlRqNy0FZsQqWkrNH2wTolpIjCNxKVMKNrITtnyAEEJEpNAEWkqFT6S6opdpMOKCq0l56ZIog7IUaEQ7a9XKEBV4lMmPGWiUyQ5BYITJDcBYpMVlLIoTZHQzA7tdNKYvYnElG7TUl5CxCVUWJoUoruUkj5rdgxBWfmi6+rzIdeujvDUiep0JThN2kcPi7HX44u7KTCpb99HBwBNb9pQIQqVnqLMs67kdCE3dcWmqdRUCU0fFEVlmohMkZDUFpia8lJbXJqITtGyElEpFZSKjDZESEIL/T6vX2WtyTZjwBbRb7BO+ve3KZHwdGE459pkRWhFgHLlJ1qeKz2H5/myU7ddT4nghMhNiNiUSU0doalDXlSmSErqSkzZ87RMXqrEJURaQoWlyTO/Lxnp4kVoX5SVNdqej7xySOh1yXvG16kyX0dymghOnfY3YrxMSmb6oP9G/eX7r5KdvEyozluWULlpIzYhUtNEaLqOztQVmS4kpo7A1JKXOuJStG5NYSmUlQJRKROUsoJ0VcE8pOBedzTkyXZruXMcs73wAr/tLSrPny2OFa5TJkJLAcpc97T4FEtPgewUiU4oeYKTkZtgsamQmqIoTZ7QdBWdSZMnMnUlpihNZfJSJS5V0lLnGdykUL5OAemjENxVWSOEovJIm46X6vZAFlplPrQtcPbezZZT6vbWKsbJtK5ax4NmthkYs7tG/fn7qfOWJVRuQsQmpCpamdR0LTR1KZKfLkQmT0xqRWECBSZYXkLXy5uXU9ivIytFopJXyC0qGJcVqstkpEo8Qnp1aROSX5f4hEvKkcx21XnkrEwQSgTJSiQn73pG8+eZeQelJxGepexUiU6e5IREcrJyUxC1yRWbGlITIjQhhEZlsiJTR2LqCkyZvJQ9n0OemaGF5qYF9ym+ce8rzfkiEX6svDJK2fWrIzrZtDUVnCq5KYvaTEpqTOPMTOhqdU9XjduKaNLOJSGbqXQhNyFiExqt6Vpo+qSOyLSRmKYC01heGopLrrRkCvd5shIqKrnzCgQjLwMuk5EiCelCcAAWHdQ73rNwMZ95WeHhSMmyVeYtBeagvByJ5xf0GnRAJA4KUF4UKCs9eZEeWywCRSdHctpevpyoTfJ7rZKaKqHJ0iQ601RkQiWmrsAUPUOrCuBV0lKnUN2msL9pvZvlUfwcDz9vTcQnW2apU5U+pIF+SLX5qpolZWLT1yCtm4KZfQ3wauA04OPAw939cznr3Qp4CXAq4MAD3f3jqeW/BfyEux8fT98aeClwMnA18Ch3v6wqPZOSGXdncX2z7hnnh8MGi6qi7Vgw0LQxfzO5qRKbqmhN10LTJ3WqpDUVmSCJ6UpgquSlibhUSEuIsByYzhGFrFzkyUSemNSRmyLxqJKJhVW0Q6shIwf23SJLndP84TmblRcs5r4LOV9rX6ZWxSlPjvJkaFVgjhyQn+w2s8y1zEZ68mQnuSezEZ0VySkSnDrV1EqiNrnRmozUZKM02WpnfYxBUyUyIRJTR2DqyksX0hJa6O5CTJqWL8ZAURmn7nnJl4L6va2G1joJrUpfVYW+Sm5CxWaIsstEeQbw1+5+npk9I55+es56Lwd+2d0vMrPjgeWFMLPTgRtn1v914OXu/jIzuzfwK8CjqxIzKZlpQ5NMqokAFWUcTSQnVG5CxKap1JRVPyurdlYkNEXRmb46A8iLymRFoi+JqRSYtvLSQlyy0lJXWKpkJSsqVevniUmeVOSJSO56JVnbIq9Un+zLZ9G7owrK9tEXcwIb3FpOXmfF2+dJVJ4czT0jKr4gLUFpATogMUvpKReetOxkIztJFCf5vCI5FYJTW27SYlMVrcmRmqIoTXZZnejMAVGpKTIhEhMqME3kpUxcQoSlTqG8SynZvXb4N/Q7x1WXH7oq4zR5YVvnBW3efVAlOFVyU9X2po7YTFFqnPDaBx1yJnDP+PPLgLeRkRkzuyOw4+4XAbj7l1LL5sDzgB8FfjC12R2B/xF/fivwZyGJ2RqZaUJV5lBHduqOAwN12rtUi00XUlM3StNXhCZvjJm8qEwTkQmpUtapxNQRmDJ5aSguZdJSJiBlslIlKmn5yMpJVkzypCQrEnuec+/lyMYiZ71ofsnLgL3wB8TCLXjdOsyt2q7mJZGZueWISY7wZGUnK0XZ/aQlaOYLjs1SYuOryxLpKRKeItnJE5205KQjOXmC05ncJBREa7JSkxel6TJCU0dkmkhMqMAUyUtTcQkRlrqF9iFkZHFsP43zQ+1rhbT9DkUy1KSMU+eFbeh4LlU1T6rkJlRs6ryUrTuo6xZyU3e/Iv78H8BNc9a5PfB5M3sdcBvgLcAz3H0BPAm4wN2vMFt5dv4z8EPAC4gk5wQzu4m7f7YsMZOSGd/rPmMKeeNRRFFGECo5Ie1TEuqKTRupaSI0dVhH25kuRKaNxDQWmJLoSxN5KRKXskhLkbQUrRMqKyvzM1lPWj6ycpIVk7SUZEUkTz7yJCNPYIpkZHev+ve8CFinLfNZecFjp2B5ngwdEJPMOlk5Sq+fFqG0AKXlZ2X9tPTE+01Xe0tkp0p0ZqlrmywvE5xQubHksFViUxCtSaRmpU1NTpSmSGjS0Rmbz5e9q4XSRmSqJCZUYIrkpam4hApLk/JAWjTWwbqOVyZNVeepruyESk6I4FTJTVVHA3XEpmlNk/FjTdt7nmRml6Smj7r70eVezd4C3Cxnu3PTE+7uZrlv3XaAuwN3Aj5J1MbmLDP7S+Bh7Ed20vwC8EIzOwt4O3A5VFdLmJTM9EHTH3kZ2QwgRG6aiE1fUlO2/yKhWXd0Jk1IW5lORaYLiQmMwBQJTBt5qRKXEGmpKyyJkKRFJVRS0oKSFo4DIpORkbSE5MnGYi+7fo705MwrW7+IvRrlmVmNZ+nOLD9qM8+Zn103u05altJilBadValJzU8kJUd68oQnWa9MdNLteRLRWUpMRnByIzgFcpNue2OL3XyxKctScqQmN0pTITR9ECoyXUhMHYFpIy6hwtJWGna/1G17pq7YOT4sOhzy/YuEp+wc55WB8q5ZiOBUyU0bsalTlqmSmmkKTSOucvfTixa6+32KlpnZf5rZzePIys2BK3NWuwx4r7t/NN7mz4C7EkVybgtcGkdlbmBml7r7bd3900SRGeI2Ng91989XfZGtl5kq8n7kdQWnrtyEik2o1FS1qakbpakrNF2SV8XswDoV1bzqiExZNKa1xHQsMHXkJS/iUiUuedKSJyywLy3peXmyUiUq6XlFcpIWjrRgVMlKWjB2FwfFJG/eoqC80KTW0Mq4lA2qOxf97Oez7HpeOp2Wp7TwpGUnmT9fWb4XL9uDRXQPJYKzlJVkOiM7edGdItFJqrCtSs6q4OzLzS57s3kkN7NDzPaO4bP5MnKTFpvVk5YVm1RVtKIsOJGaoihNgdAk1Klulo6+pKuYrcwvEJmyaEyVxIREYYqiL0UCUyYvVeJSR1jWLSfHvhgd79AJ3bZdqPs9yuSn7PyFik5oFKdKbtqITROpqVP9bMuEpikXAI8Bzov/vyFnnYuBG5nZye7+GeDewCXu/kZSER8z+5K73zb+fBJwtbvvAecQ9WxWybRkxr3R25cu6qymCf1xF5H86OtEbNpITUiUpmysmjGTjcq0EZlOojEdS0wTgWkjL3kRlzJxyYu0JNKSF11J5GRlWTwvT1byRCVvXpGYpD8vVtZhhbRY7C6SAnv0P2/Q+jyp2d0N6DkAWCwC2sDMw6I+OzsH1zsgM5mf9k5qhZVbdH5w+0R80gKUyE9WcFanDx2QnRDRmdsexzjEnAXHOBQJTtx5wTE7wpzdZcQmuS+jiE6R3KxGbtJRmyKxSc6ox+ctKFoDB6I0RUKTF51pO4hm1yLTVGLyBKapvIQ865sKSyIefdDXvkMlqeicVEV4sue7S7lpIzZdSU3dGidVg8GOCTdr1QtnQ84DXmNmjwM+ATwclj2UPcHdz3b3hZn9AvDXFoVg/gH4vYr93hP4lbja2tuBJ4YkZpol2JpUZYptZSf9464jNnWlJqT6WdMoTZHQ1I3OdEmdrpaD9hfQ2D93WUE0BgJEpo7EVERh8qqQ5UVgQgSmLPISIi9F4pIXaclGWXaXolItLGWykvzPk5Q8OUlLyco2KQlJi0ZWTrISspvX81PFAzBEZPIok5udnDeI6XnZbRMBSs9PS1EiM8ltuTM3wPZv6Yz0ZIUnT3byRCepyjbfiw4UyUy54CxsHiQ385TEzPaOsTfbWYnazKAyYpMrNXv70Zjl//n8QJSmSGiytOkMoGhgzML1C0SmKhrTpcS0kZc64tKnrAAsvlK9//lXdVu4LPtOIaKTd/7qRHFC5KZKbKq6j+5aasZWptkk4gb5Z+TMvwQ4OzV9EfCtFfs6PvX5tcBr66ZHV47wH20ITcQmVGpCozR9dHEcSl54dqhxZ3LHckmWhVYtC6lW1jAaExqJKZOYsihMSASmSGDqyktdcamSlrzoymK5LP4ui2R6VVaW6+VISv68VCEv9XlVbNLzM+0Ncgphu8cK2hMU1VOrYJYNt6TYOTTPTK+uO09tmyc5ybz8ZZY/PcvKTrROnuikJWdfauJjznwZ2ZnP5iuCE0VwdpibM58tmFvUbfbc9tgjvp9svoz07NmcmS9Y2M7+59kO871IYPbiamfAgapoZRjA3i6+s5MfpakhNGXRmeXxGnQCkFAUlelLZNpKTJnAhMpLG3EJkZIh9l1XhPLOQV3BCY3chHQ00EZqygb5LKt90oXQiOkyqMyY2f2Jul+bAy9x9/OGTE9CV90q7l67WztS08Xgnk1+9NHxx/3Dz7aXqapitrqs+DqUtpGpoo7IBEZjyqqThURi+paYNgJTJS9l4lIlLVlhycpKnqgk89KCkshJWkj2VpZnCoCpZYucqkJFYtOWrMQAzFP3+YrEpNZNxCg9LxGgZJtEbNLCc3CexZ+9UHLmcVRnZx4vS0VxZjO4jn2h2ZkZ1zNnPvO4ilokN3Nz5m4cEBtgzkGxmbO7lJm577I3n8fRmuja7OUITFGua4sFPt9ZidLY7m70W06iNFAqNF2S7ZIZynsMi5YXVy1LUyYyIdGYdUlME3npU1r6IC+9bQSnjth0JTVlZZ+ysk5ToSk+Vng1+ilHZ6oGg950Bvv28YA5LwK+j6jHg4vN7AJ3/+BQacpjcWxvdEIzlh/cWNJRl2BhCW3sD2sVmTrRmC4kpk4UpiwCUyYwIVGXEHnJikuRtJQJSzIvT1LS6y8yBctFVnLyeoRq0S4izTxHzuep+3qeEpX5PC0t8wPbzzNyM5vPlp/zJKdIcNJRnJ0d4zp8KTeLTPQmufY7c2dvDrOZHRCb+cxZ7M3iKmnG3JyFw9yNuc2XVdEWzKIOBRyw6N6ds9ivJ5ZQkY0nnQVAdvyaWGiSbp13dlaFJjpRq0KToio6k0y3bTeTJmSMjLI2MmXjxETLuxeZriVmagJTRfr7NBWbrqVmTEJT1dlR6H7ENKksiZrZHbOCYWb3dPe3tTz2XYBLU122vYpoRNFRyQy0F5qxUfdHP0XKqpitrJftIrnOfqu2zVnu2dbYrFYt259XHJFJ6ENk6lQnK4vE5FUjC5WYsghMWfQlRGASGcmKS3q9rLikpSURlrSoZAXloOTkFyx3a7Z5ANhJ3jBeuz9vnolIzudz+EqyLCUtO3OuvzYWnWuvX0pOIi6LRdx2ZT7j+muPsXMoWj8tN4tDs6XYpOVxZ2fGYrEvNotFFJXZJb4fZh6V5ReR2MznSdsbY3dhy+pou+zfN/ttbaL97hJ3JsAcWMDePBKXZEycKEi0/zn+n1Q9g2hX87jtDLBS7SyZLhUaSFU5S3o7i0Vm5ULlzJsoIVXLQlnHmCubJjF5LL6yaNQm59gXF532ttZWaMbIVF/SbjshV+w1ZvaHwK8Bx8X/Twe+u+Wxbwl8KjV9GfBd2ZXM7PHA4wFuWlKNaMxM8Qe9jYQ2+i+lphyl8QABK2vkH0JVRCaPsp7IyhrzZ6MzcLA6WfQ5Xj/daD9QZMoiMUVRmKIITBuJWV13tRBbJC15VYaq2L2+9iYN2DkwVVx8Te6btNSkum+eW3S9dgx2939WK91Sx7vYXRg7REKTtK3Zv3dmK+PgRPfgYmWsm4XPlp0DLOexsxSa5bzZzsoAnUnnANHn6jY0heQITB9VzTaRPqqWbTpNhSaU3S8tgse4EcPiDNKb2agIKQl9F/CrwDuBE4BXAHfrM1Fp4tFIjwJ8w1fdoFlXQAMzNpHZ9KhMU6KqH/G52d0vefmxY/sD4i0W5VGfpJejAmx3Nzc6A8nb3/x9294Cn83jnpkOxQ2a53EPTeH318wX7NmcObss2Il6iGLOzPbY89lyem57y+jMfLZgsTePq/kYc1uw8Gia2R67cVWgqEqQs9jbL4wmn3f3LGoAji/fxO8u7EA3wotF0qA8/qnH1cvyuiCOv1Fmem85f2dnlt/jWPw/e9bm88MsFnvMD+2wOLbLzqH5Umzm8zmLxYL5oflSbOY786XcJNsk60bfJVm2fz+sik7zfCEbjUkfN0nP8nNO9bNsVCZZP7TK2Wr1stUqaNnqZune0Xbmq72iJe1osp0EZHtAS3o/S3fvvN9+Jun5bI85i+V4NcBKb2fLeXurajZLTc/2jmGpAThXejdb7K9neb2T5URiJDJh7Bw/LxWaQyfMJTQZ+hQZqK5mBt0PeyFEU0KepseIKi18FVFk5mPxYDZtuRw4NTV9SjxvdKzzB9tFBwCiXDrS0hLVWW8YTUmLy2Kx2m4mu3xvsdJuJpq3C7N4sL64qlkiNKujlq+mr0hosvPne7ssZjv7AuO7LGynltDsHzRJc/IdkoLFHGbZ7CDV8DwWmeUpwTg8c/b2WArNahuKKFITVVVKpp3dXZgftqXY7O56vE5cuM30zJVITDR/zmLhHDkSzT9y3M5K1ObwcYcORG2I5x0+LqftzHHJ5c20n/mqw6n1ctrL7C7guCMH5jdhnlPXu6qdTLTOqrSkl2c7BthJVSmr6hAgKzCw3xnAfm9n+wID9SQGiOQl031z0nUzsBSZJDKTiMzc9wffnO/t7o85s3dsKTLpsWkgKzGpa5kWoUR60hKzyJk3AeaHd3LHloGoS9x0VbOdIzuNq5rND80Kq5p1JTRJIX9Tq5u1kZjQKmZdRWTG9iI3hGlWMbOVoRS2kZBvfzHRyJ53Bk4CXmxmD3X3h7U89sXA7czsNkQS8wjgR1vus1O6kJg+fsxj+bGNJR0h7B3bLe3RLKF2dCYvEpOelyM0K9GZGkKTvDEuitCUUthdU8F0tu1Bdj8pqZlbVO1nPl+wcGNnGa1hJVoDME96NJtFVYiyYgMwn8XV2eJDzBfGkcOrnQIcOWxxVbRIcCJR2a9+duTIjN1d58iR+UrVsyNH5rHozJfrJssSstXTos9JwejIge6Vy3o3W87rqGE35Df+h1VBgfwezNLzs437IbwXs2Q6LS7RsoPykizP9mQGxQITLdtbCgywjMREnxfLSAwQJDGQHVBzVWyS35ctji1lJhEZW+wuRWYlKpNIS0lUxpNoXLxdMq5M0jVzMt1V43+A+eFDy04AZjuzZY9maRHJSkqanSPzlU4AqoQmeQmX7gggefZlOwJInqt5UpMUooukJimM15GahCnLzToEJqGriExV2afsxW1Z2aKo0X5RjZOinszU+H+zCCmNPi4eBAfgCuBMM3t02wO7+66ZPQl4E1HJ4qXu/oG2+21LV1GYuhITGpEJEYiyH2lZFbMufvTZMWa6ZG+xWOmeee/63ZXumfeOHSvtnjlNk+hMpdAkpe35PEhoIO4QICU0QNxz0r7QrKQhJTUHKljVkJoD0Roy0RqLozXE0RpbrX4WicucpdLsxSIRt6uZJ21z4oJp0rZmnmpbc4TV3s4O70RRnMOHfKWzgCOH4sjNoajK2RGM3QUciQMh+5IT/9/NGUxz10kiNKvzoGg8maIxZ7LLouV5Y82EB7DTQpTX5XIe2fFkIEdoCgbPTM8vk5WV6ZSwwKq0RNOrUZfkf9Ugmll5AXIjMNH8YoEBGkkMRNU49wVmX2SWVcvSIpONyCT/O4rKlFVNm83nB9palUlJFelts9GZukIT7WN2oGezPqUmoYncpBmD6HRZbawPgYH+JQa6FZm6TOkFrVil8sqlRCY97w+7OLi7Xwhc2MW+mtBH9bEmkZgxi0wXNB0wMysrdclWNSuLzqSFpig6A4ERmqTKWVKoSTcM3jm0XxhKR2kSCUlJzfKYNaUmj2wkJ71hiNhEqyfVeZJxPeKG87HcRJ+T3s/K5QYiwTnMaucBacFJ5pGat5cWlAOdCRzsHQ2SMmay7mpPadExVs9V3sCaecvylu+v12+vTTsFLw3SwhKtZ7nLVubP0uvH/+fp7ZJ5q+unhQXIlZZken9gzH1xAQ5EXqJ1VuUFWGkLk43AAIUCE/1frU6WzEtHYlb/F0gMBItMaFSma9LdO4dEZ6Jl9YQG9ns4S55JVVEaqJYaOCg22UJ2qNwkhLaz6bv9SZ807Z2sTlWysUpMtF03L2inLDLOfmc+28p0r14F62rn0rQaWZ22MW0lJtpHsxu9+E3IOG+dbHQmtO1MNF0iNFCvylk2SgP5UpMXpYFcqaliHncSkGZFXpbzDorNnIPtawBmqdHVk7ffSTe3idwcstT4NIngWKo9ic+B3WWvaNE84/AsXrYUodWuniG/l7Qy2YF84dn/nN42vV76DFl2qJClBK3MK7k8OYGazsh2mpCQ16/ETkZuVnoWT31eEZq5k9QzLBKVaBtfmVcmLNH/g9KyOn9VVtLyko68wKq8AIURmGRe9H8/CgMl1cmgscRAtcikyZsXyuzwDnvXH6wy1oXQAEupSZ4dIVITrZccO/U7zql+tlw/JTfZ53aV3Cz3UVNy0oy5Y4GuulGu2wYmtPwUUv5pIzHQTGTq1jIpSkOfNU1Et4yzRFqE2eC9Z7RtA9O1xEB7kemqTukQP/y60ZtsdCZYaOBAGxqIB8RLCkJZcYHVjgFypWZ/95YqHHt6fs73KOv5DFbFJi0vwIFOA6J5q8uBpdwAK90+zpaFz5ScxIJziGMsmK8IDhbNYxYda3UMm1iQ0qIzSwmHrwpQQp7wRJ9Xz1ae+OQtg1UJWq6zOHj28+btH6NwUScUiQzsi8fqvNUEzTLb58lJ2fx098hZWYk+lwtL9LlYWqL/4eIS/S+XF6A0ChP9D5SY9OeCaAwcFJk0eVGZLtvLZCkTGmClDQ3QSmrS+03vO/vcayo3y+0DJWe5z4qOBaZOmwb7dcpUoeWfPgUm2r6exJTtUyKzGUxLZtZIVw33m/RO1pXERPtqVrWs/G3IcLdNtt1M7joV0Zm6QgMER2kgR2pWBtUIr362TE8NsVkd6G//c4jcRJ/3BWc22z+H6ejObLb/fRLJOcR1KaGJ58eJXbDDIWKRiUUHVse2WczS0pKa76vXeik981W5ya6bXZaWn/195c3LkZeceVXb9E1aMLLs5CzLrj/P9EC3k5nOE5QD82eLA+vMV7pFThV6M8ISfc6XlpV5GUGJPreXl+hzgcBAcSQGwqIxsCIyedXL6kRlVqIwqXYzIdEZOCg0QGmUJlpeLjWwLzbp50WV2ET7DJeb5fYBkgPlbXDqUjX+TR/0MbZLk5fAdcpAIeWcvgQmOn63ZZnpiYyVjhW3DWydzPTdVWCf8gLh0ZKm0ZiqY9T98TdtL1OHkOhME6EByqUmJ0qTHAsyUgPkdhKQR+arhERs8npDiz6vyg1E7W3mmZ7R4GB0Jk9wIExyoEJ0Ul+gSHbg4GCei0yVuUV2uR+8dxd7c7LVibOyU7Rt2fp55ElTW7KikUdaMlbnHyyM5a07z1y7vEjKcjpHUrLz52TEJD5knrBAmLQULc8bFyava+WVapshApP9XCIx0FxkuozKhApNtO7BKA1UR2ogXGzS2+c9O4oiN/vHqZYcCBed3GOUdNQx5kEjh+ptdV1tfKN9jKMcI8bPpGTGZv3LSAhtx4KpG9noSmCg/MdfdayuMoCQLpLbktezWYjQAIVRmmSdsigNFEgNhEVr8iioigarA3CWyQ3QSHAgkpz9z6s9pqUH7NzJWTfhENexyAzumZYdiKqqHcouzzhEti/9Rc6oxwfeUM2SbfPv7awQrS6rV5hJV5drS1YwStfNkZb9ZQcLa2nxSJgFrFckKVAsKsDKQJV5QhJ9rl6neEDLksgLrIwPkyswUB6FycyvIzHR57AG/6GDbBZFZ+Cg0AArbWiAwigNFFc/i9YLE5to+3y5Se+n6JlSJTnRccNEZ2W/1x4Ux6GrrvdFm7JS3TLOumqTQHOBifYtidlUhjeDEdLlwJVNq2TVaa8ylMRE2xWfq76iMnlVzfKiM6FCA7SP0iTs7KwUbtJtaqAiWgMHxSZdFQ1KozYH5Cbu8nk5XSE40fShlUJiWnIAZpnISFpeoundzHSx7Oyvsyo1QKX4LNezg/fmXo7gLNfPy/KWEaKw31xpOL/HZ2KebOSRJyr7y/KjAFkxgVU5Wa6T3V+mK/AiCYmmw9ZNCwvkS0v0uUBcoJ68ZD9DfYmBYJHpIypTJjSQH6WBg1IDBEVrovUOik20z3y5ifZTLDjpfZY9c0JEZzU99aWnLnmSVId1vqRtWr7p4yVs32WX6BjjKr/0gXoz2zCZ6VJC6tKmHUndhvahPZOFdLe87oygKCrT1wBWIUITrRcWpYnmFUgNFEZroIHYBLE6aKddv4AV4agWHKBScqJ55aID1bID1x3oXS1ab/W+2FnOL5YfyJGbWfH9umfzA+tnyZOjKsrkqSl54pCVjSxZ+QjaZ7JtzvhEs72D62cFJZpXvW12uzJhyU7bIrtuoLhAsbxkpw90X5cRkAYSE00Xi0xdDghLZsyZPKEBCqM0cFBqou2KozWQLzbp4+Q9d0IEJ2+fefsPedaWtc+pS7aL6TymICNljOWlKwxbbomOPx2JEftMSmbMbBBh6bLBe5NCe51ulUPHi2mTGUTbj++NRlHbmaZCA/WkZmW9TLQGaooNNJQbDkZvcgQHOCg5sBSdrORE88pFJzsPwOY5BeIcmYF8cSmSn/1t8iWoeH95+yiWozLKxKkteXIRQp6AHFynuMpT0aCrRfvN21eVqOTNywpLNK9YWqBCXKBcXqCWwEC5xEB9kekiKlMlNJAfpckeP51nl0Vr4OCzoUpuov3XE5z9fYeJTvaYbZ7ZeQOBTomuyitNXzB2XV4Ji/A0L69E6Zi2wDhWWl16G5iUzDRl3b1vtYky1B0Pps5gl0NnCutoK1OHIqGBg2kNkZqi9eqKDdSQm6r2NktWIziQJzmQG8lJyIgOHIzo7M8/KDzR/EO5hVvIFx+I2u5nx89JUywt16XWqf+bzIpSlqHu5iLhKF6/XHDK5AYOyslyftF1zJWXAFmBA8ICOdIC1eKSNy/vxUBdgYFKiYnmdReRSZMrKzlCA5RGaWA1j6sjNtF+6svN/rbFEZz941aLzv7x6glPUXoSxjqGWlu6qv0wdBklSkOIBLUTmLGVV0Q507paZoNmNF1WhWo6iOUmZgxdntc60RkoHlSzTGosk94yqYnm1xObJF1pcuWmEwokB3JFBwoiOpBbfS2hSHqiZfnis7+8WICgWILSJN+kTIpCCI32tKVKMKooEpAD65Wc15B18q5nNL9AuApELEhYEkLEBYLkBfIb3VdFYSBMYorWC43KzA4dYi+TlhChSdaDaqmJjhMuNtF+wuUGyO36OUtVJCfNIvWd6j5L0/JTlp5toWlZJKFOmSShXvW00Nom1ZEJCUx3mNnDgOcA3wjcxd0vyVnnVODlwE2JKkofdfcXpJb/LPBEYAG80d2fZmaHgd8FTgf2gJ9397dVpWcrrtwQmdW2ZBBtM4eyNNcZDDOEMqEBCqUmJEoTzU9JSQdiAxVyA91VTcvloOhAtezAqvBAsfRAfsE3r3rbwXXKJejg+uVSFMKMa5f76oO26Wu6r6JzvLpOSQG8JFqUKyrL7WoIS9n8QHGBQHlZ7iNMYqL5YSLTBaFCk6wL+VIDzcQGyuUm2m+54CRURXJW91kd1cmjjfw0IStMbVhHerM0KX+kaVadvs6L2vbiAhskL16/580OeD/wQ0TiUcQu8BR3/0czOwH4BzO7yN0/aGb3As4Evs3drzOzr423+UkAd/+WeN5fmtmd3b208dqkrqTZsG9Rus5U2mQY9TsNGF9GMcS1LBIaaBalSQiJ1pRuky001ZQbqIjedCo5CfmyA2XCA2R61MqKD2TkJyFAgpb7nOfss8H395z7IdpXH1GyfJqk++A+arbPCKjKViopUCwqCWWRxjrSUrFNUXfHdQQG6klM2fp1yYvOQLHQAMFSA+FiA+VyA9WCEx0jM2BrgOjkpbWM7PdoWzhPs7i++rcxhIBAt98zj3ZV6Ju8oA1vB9KlvGx7tC4Ed/8QRG3ZS9a5Argi/vxFM/sQcEvgg8BPA+e5+3Xx8ivjze4I/E0yz8w+TxSl+fuy9ExKZqoYKgPJo6tMpXkjvG7lBcIbyYVkGFXfq+uoTChFQgP5UZrldjWjNelt8rbLi9pAfgGsUnBgzZKTJjW2TgFL8YEC+UmolqDlPkPalQQ04K8tAUPRsMMACJCSleME3Csh1SGr1mkgLVA+TkuhvEChwEC5lDQRmU4HycwRGgiTGigXG6gnN1D8jAiRnP1jhkV00jSVnuI0lPT8N9CzqQv6KqS36821XsP1Oo31JS/jwcxOA+4EvCeedXvg7mb2y8C1wC+4+8XAPwMPMbNXAqcC3xn/3xyZsZlNMuRaRtsf0Rjediz324HEQLXIZMeYqUtZdAaqhQaKv2uR1ETLwsTmwPZ5Ba0WggMBkgNrEJ08quUnYUWCoEKE8ggrVJZJU5/Ukow6BLanWaFOe62QdUPuq4D9NJYXKBUYaCYxfVEUnYHiiAus5pV1xQbqy81yvUDJgVXRiY5Z/XyqE92pIitCURq2r3DbdXvkNgNR1u1drE51sdBrO9QL1SY41nTA5pPMLN3W5ai7H00mzOwtwM1ytjvX3d8QehAzOx74U+C/u/s18ewd4GuAuwJ3Bl5jZl8HvJSoHc4lwCeAd0LJ4GmpnU0Hs8m8Fem2s4BxvvWAbt98jCnzKBMaKI/SQLnURMuLxSa9feE+CqqlLbcvERwoLwQGiQ4MJDtlZMbs6YgD0jRF2hS+m2wbel8E7rvsfl2uUyUv0EpgouUBgtWiepnN54XfNXkB00RqoJ7YFO0nLx/Pi2KESg5UP3OyshOlo35BOStACZvae1nXtJGTNG26Qa7bxqVOOWxM5Y81cpW7n1600N3v0/YAZnaISGRe4e6vSy26DHiduzvw92a2B5zk7p8Bnpza/p3AR6qOs5VXr4p1vpXprk/4ZhnNGN6AQHhG0jYqU4cQoYHyc1IlNdE6+e1rsvtICJIbCBIcWJUcCBQdCCuIJt+pjvR00XPbbnF7nq2jqyhCE3GteewQaYFAcYFKeYFw+ehbZBLKhAbKozRQLTVQLTbp/SzXK5KkgN7JlutWPD+ayE6WPPmB7grjeRSJ0rrp8zuW0cU4LW0a49cts22iuCy8uO3KUFjUoOb3gQ+5+29kFv8ZcC/grWZ2e+AwcJWZ3QAwd/8vM/s+YNfdP1h1rEld0aE7AAihr7c8bTOppplNn29CoHuJGSKTqorSQD2pidYrqQYXIjcQJDgQLjlQXdg8IH+hhdn0920b7QmNLG0jXUXSGp7bUFlZ2SZUXCBIXpb77VBi6uwvhBChgeIoDVRXIVuulx1rK1BuqvZd9iwoapMS+rwpa3fU1QCIRVKUx1AS0ZZ1DBbZRY9hTct9dcsD63xZOnXM7AeB/wucDLzRzN7r7vczs1sAL3H3BwJ3Ax4NvM/M3htv+ovufiFRdbKXmtn7geuBx7i7xz2YvSmO1Fweb1/JpGSmS8YSWu4yE2ybMTXNdPp8K1IncxnybUuI0ECY1ETrhYlNep/765fsO1BwoLwQmSc6EFZYzY12NRWPvHMzqqpvE6BD6WsiK8tt60hLQs12RXWFo4+2MbNDO0GdAFQJTbSv/fs/VGygntxAseDk7TvkOE17JVtu36KAHNoBw9RHhe+Svrow7uLldNPnvsSlHe7+euD1OfM/DTww/vy3QG7IyN2vBx6VM//jwB3qpmccJfpQBh40s4h1vJXpMmNdZzgXmmU2dTOaWoJU0vg/S1VVszShQgPhUhOtGy426X3vb1NxjKICYUnD96qCZ5HsQP0Cb+n57zP6MrZqagNFmtoIyoF9NRGWhIYdIjSJmKy7gX8Ryb0fcg1CxQbqyQ0U58dlkpN3nAPbVx235vOmrBey5T43ZWyRkdFHrZm2LyibCsvUqqE5NsQ4M6NiUlcsqmY2zXBulj7f+gwZ1oX1vimpHUauITJQUZDOoY7QQD2pidavJzbpY6QJOl4DyVkeM6DQWiY8K/tqUZiue/1WGEmBtiu6lJLCY7SRlTQte3JrU+VrHSITGp1JqCM10f7DxQbqVyFbblfx+2orO3XSAt0XqEPkaKoMVWW/S1FoG12ZmrSIYnQlazJk6LnrN0pDhnihXUbUKNpTU2TWie8ugoVmf5v6YpM+Xppaxy4raNboxji04BsqPbnHUJWzTuhMUvLoqAvqLtqsrDMik+TnTaQGmolNQojgQHPJWW4fkMdXCU9VWkIITe+BY468je4QrEMAuqwC1qqMMuIyg1hlWjJjs42ox7qOMHfXmXAXGdi65WW5bcMMqdVb/QbUjdKsbttcbNLH3t9Hw+/ekeikaVqQbiNBm06vclJED+PmdNnovq3I2M68UXqaSA0czJ/qiHtRnthWclb21aHwHNh3zZcUegM/HH22Tenquk5dWtzH2ZvZOtEvPIex1qnt8y3RmEK/0EFd2ZaZ07pFpkuqunoO20fDqmll9CA6ZfRVYB9CkgaRj6b0Ndhnii4lpkuaCg2sPnfqig3k51l1I5Nl+Wao6Cz3FZiHN4madF1AritHm8yQDeP7ks6py4qoZpyl9gLMxisaVWxC/dSV/Xac4XUS+ekgw2orMV3cn02qnOXvp73UrO6vo+hNHlUF4B5kpymTEou+WIOw5DFWiUnTRmgSsvlIE7mB4vysSfXLkPy1rvDAMNXHDqRhwi+vxsY6o2BdS8qUX2JuO+MpIfTIptR7XWsm0dOPutMIUEcZWVcZ2FhF23ePdSY0q/vtUW6yhBaeRyQ9k2cgYcljChKTJv1b6CLtXclNQlme16adWd08uYn8rBxvTc/ErqRpKKZQzW4d0ZPNlRVjd28zOsdqyvjv8DRmkxOTMWUi63j71Mf37SOT6zJT61pi+pCCvoRm9Rj7haBexaaMOgXwbRafEYlKGeuUGNs51EsHANnfQh9yk6ZP0UnTRecaXeTtbYUohDE9x8fIkNW4+hSUsb6gFAeZ1JUys63PVIYKh6+lB5OeMsQ+Mru+MrnBJKBjRiE2VbQt0I9FhiYiJlMgEf4+ezXrQ27SVOVNbWUnoUm+2kfvgmNrD7EOuYLxfe8uWGfkZJNExYGFIjOiD8ZeB3cIKZx6GLnPzG+0Bf4OaNNL26iRRGws6Shm3901F/0u+opKheZjXUlPmq7y5zF3ub6JklHE2KptbZKgiHpM68rb+CVhXYwlQrXujHtdmee6MsV1FfD7rmIWQlcdG4jtoYvG9O3TcPC3s47xaKp+K32flyZ5YB8ClMfYCtGiW4aSEj2fpss4SsQbxlhEoy5Dv1Ea4gG17kxziMxyDCIjRFOS38zQUpOm6De1zkE36+Ql6zp3Xeen65Ij0ZwxR0O2RU7cYbGncWamg9rMFDK0iBQx9Bu0bX3DI4ERm0bfbU26oOp3t07ZSdM0Pxr6HI+5oFxGqIRN9futm6Gfp2L86JfUE2OVi6YMLSV5jOFBMIZMdgriMobzJDaLdbc16YLQ3+pQ0pOly9/tmK9L14zh2TQkU8jvp/DcFOFM6hdnZhsnCX0zRgkpYowPgLFlylPKgMd27sR2MHRbky5o+jsfiwTlMbb8YAr3QQhjO6/rZkrPxD7ZVTUzsS6mJBZNGaOQ5DHmB8AUM+cxn08h0oTeq1Ms7HaVd4xZirpCedZwTPEZJ8bNNEqeCWZbIQTrZioCUsSUHkpTzsSndJ6FaMtU25l0wRD51DYI1FiZ8nOpDnqGbS7TLsVuKFOXiyZMPZPZhIfB1K/BJBjLQJtpNF5Op3T1O9oEKarDJuShoj/0fCrG3dSb2dAJqEPUZmZSSd5oNjVz2bSH6qZep7UwRvnomnV+R4lTMH3/brdNlkS/6DmzXZjZw4DnAN8I3MXdLylY78nA2YAD7wMe6+7XmtkZwPOAGfAl4Cx3v9TMbgW8DLgRMAee4e4XVqVnC57U24UylM2TkSy6xjXZBiGZCn1eC4lSLaaYj2yTgE3x+oyaDX4OOIN0APB+4IeA3y1awcxuCfwccEd3/4qZvQZ4BHA+8DvAme7+ITP7GeCZwFnx/9e4+++Y2R2BC4HTqhKzuVe3AGUQ42TTBSQP3YuBbPBDSHRI3/eJZGlwlGduEcr3R427fwiiGlMV7ABfZWbHgBsAn052Adww/nxiwPzKg0wKZWbrZRslIxTdi4HooSQ2gSHvY4mUGBrl46Im7n65mf068EngK8Cb3f3N8eKzgQvN7CvANcBd4/nPAd5sZj8LfDVwn5Bj6e4cCZKG9SABqYkeYEIMzzb8DjdB2LbhOm0AtoHjFe7tNdrsJDNLt3U56u5Hkwkzewtws5ztznX3N1Tt3MxuDJwJ3Ab4PPAnZvYod/8j4MnAA939PWb2VOA3iATnkcD57v5/zOy7gT80s29299JvOK1fnpkK/RuOZKMD9EAVQkwN5Vsih00UjxFxlbufXrTQ3YOiIiXcB/iYu38GwMxeB3yPmb0J+DZ3f0+83quBv4o/Pw64f3z8d5nZccBJwJVlB1LuIVaQTKwZPcA3Dj18148f0xglQqwD5W+iBp8E7mpmNyCqZnYGcAnwOeBEM7u9u38E+D7gQ6ltzgDON7NvBI4DPlN1IJWkaqCC/hYi2ZgUetBuJ9t63SVx02Zb71vRLe6wu1hvb2Zm9oPA/wVOBt5oZu919/uZ2S2Al7h7UoXstcA/ArvAPxFVZds1s58E/tTM9ojk5ifiXT8F+L24S2cn6rLZq9IzrZKamYRCSDAmhh7YQvSDfltC7GNzlQ/Xhbu/Hnh9zvxPAw9MTT8beHaN7T8I3K1uegYpFZrZ84DvB64H/p1oEJ3PD5EWUYHEYWNRQUgIIcQ6kGiIPhmqpHoRcE4cavpV4Bzg6Z3sWYVvMTEkFUIIIbpE8rA9DFHNbGwMUvJP9TMN8G7gh4M2NJOsiM6QRIipoILJZuCL7RnBfmzoNyTE5jIGM/gJom7ZxIYiaRDrQIUVMXZ0jwrRA1s+ZIcDi2bjzGwMvclMyGA7ZnYuUQ8HryjZz+OBxwOcepMTe0hpt6jgLrYBFcqEEEIUsuWCIdZLbzJTNdiOmZ0FPBg4o6zbtXg00qMA3/F1p7hkQWwjkgchhBClSCDEljJUb2b3B54G3MPdvzxEGsR2IzkQQogtR4V/sQk47G55c7yh2sy8EDgCXGRmAO929ycMlBZRgAr8QmwZKtwJIbYFlXE2hqF6M7ttk+3MVMAWQpSgwrgQQkwXlfFEA8bQm5kQYmgkAUIIIdJILCaBA9ve67tkRogukRQIIYQIRcIgRGskM6I7VJAXQghRBxXmhRAtmZjMmArMQggh+kEFayHExHCH3UXhCCdbwcRkRgghJoQKx0II0R96wS2QzAgh6qDCuRBCiDwkFoPgDru7Q6diWCQzQrRBhXshhBBDIokQW45kZltQoVsIIYRYRSIgxOSZlsyYqVAuhBBC9IkK+EJMisXe0CkYlmnJjBBCCFEXFc6FEGJjkcwIIcRUUKFcCCG6RflqbczsecD3A9cD/w481t0/n7Pex4EvAgtg191Pj+d/DfBq4DTg48DD3f1zZnYi8EfArYgc5dfd/Q+q0iOZEUJ0jx4OQggh2qJnSSVRb2ZrH2fmIuAcd981s18FzgGeXrDuvdz9qsy8ZwB/7e7nmdkz4umnA08EPuju329mJwMfNrNXuPv1ZYmRzIjtQZmiEEIIUY2el6IEd39zavLdwA/X3MWZwD3jzy8D3kYkMw6cYGYGHA9cDVR2PD0tmTHTD0wIIYQQ00RlGLF5/ARRlbE8HHizmTnwu+5+NJ5/U3e/Iv78H8BN488vBC4APg2cAPyIu1d2bzAtmRFCCCHEdiMhEGKJA4tFo2pmJ5nZJanpoynZwMzeAtwsZ7tz3f0N8TrnEkVOXlFwjO9198vN7GuBi8zsX9397Svpd/dYdgDuB7wXuDfw9fE273D3a8q+iGRGCCGE2BYkAkKIiKuSBvl5uPt9yjY2s7OABwNnuHuuTbn75fH/K83s9cBdgLcD/2lmN3f3K8zs5sCV8SaPBc6L93epmX0M+Abg78vSIpkRQgixvahwL4SYMu5r7wDAzO4PPA24h7t/uWCdrwZm7v7F+PN9gefGiy8AHgOcF/9/Qzz/k8AZwDvM7KbAHYCPVqVHMiOEENuGCvBCCCGa80LgCFE1MIB3u/sTzOwWwEvc/YFE7WBeHy/fAf7Y3f8q3v484DVm9jjgE8DD4/m/BJxvZu8DDHh6Tk9oB5DMCCE2CxXUhRBCiN5w99sWzP808MD480eBbytY77NEEZi87e9bNz2SGbHZqGArhBBCiITZfOgUdIp74w4ANoZpyYy6ZhZCCCGEmA4bJg9ifExLZoQQQgghRDGSB7FlSGaEEEIIIUKRLIgR4Q67u5XjSm40khkhhBBCjBOJgxCiAsmMEEIIMUVU0BdCCMmMEEKILUGFfyHEhuHuqmY2dAKEEEIMjAr5QgghJopkRgghQAV6IYQQk0TjzAghthMV3oUQQggxcaYnMyqACSGEEEIIIZiizAghhBBCCCE0zgwwGzoBQgghhBBCCNEERWaEEEIIIcSk8B0VYUWE7gQhhBBCCNEKycUwuDuLxXZXM9OdJ4QQQggxYSQSYpvR3S+EEEII0QBJhBDDo1+hEEIIIdaGBECIDnHYPaZqZkIIIYTYACQKQoi+MbNfAs4E9oArgbPc/dM56/0VcFfgb939wan5rwBOB44Bfw/8lLsfM7OnAj8Wr7YDfCNwsrtfXZYe5XpCCCG2BhX2hRCbhLuze2yx7sM+z93/J4CZ/RzwLOAJeesBNwB+KjP/FcCj4s9/DJwN/I67Py/eBjP7fuDJVSIDkhkhhBAxKugLIYSowt2vSU1+NeAF6/21md0zZ/6FyWcz+3vglJzNHwm8MiQ9enIJIbYeFeKFEEKIcMzsl4EfB74A3KvhPg4BjwZ+PjP/BsD9gSeF7EdPcCG2GBXihRBCiOniwF6zcWZOMrNLUtNH3f1oMmFmbwFulrPdue7+Bnc/FzjXzM4hko5nN0jDbwNvd/d3ZOZ/P/B3IVXMQDIjhAr0QgghhNg2rnL304sWuvt9AvfzCuBCasqMmT0bOJmD7WkAHkFgFTOYmMy4mQqeQgghhBBCDISZ3c7d/y2ePBP415rbnw3cDzjD3fcyy04E7sF+BwGVyAyEEEIIIYSYIsP0Znaemd2BqGvmTxD3ZGZmpwNPcPez4+l3AN8AHG9mlwGPc/c3AS+Ot3uXmQG8zt2fG+/7B4E3u/t/hSZGMiOEEEIIIYQIwt0fWjD/EqJulpPpuxesV+gf7n4+cH6d9MzqrNw1ZvYUM3MzO2nIdAghhBBCCCGmx2CRGTM7Fbgv8Mmh0iCEEEIIIcRUcYdFs97MNoYhIzO/CTyNgoF2hBBCCCGEEKKMQSIzZnYmcLm7/3Pc8EcIIYQQQghRA3dncWx36GQMSm8yUzbYDvCLRFXMQvbzeODxAKd+7U06S58QQgghhBBi2vQmM0WD7ZjZtwC3AZKozCnAP5rZXdz9P3L2cxQ4CvAdd7iNqqQJIYQQQgghgAGqmbn7+4CvTabN7OPA6e5+1brTIoQQQgghxGQZZpyZUTFo18xCCCGEEEII0ZTBB81099OGToMQQgghhBBiegwuM0IIIYQQQoj6OLBYqJqZEEIIIYQQQkwOyYwQQgghhBBikqiamRBCCCGEEBMkGjRT1cyEEEIIIYQQYnIoMiOEEEIIIcQUcVjsKjIjhBBCCCGEEJNDkRkhhBBCCDEtZirCigjdCUIIIYQQYv1ISFoTdQCwO3QyBkV3kRBCCCGE6AYJilgzajMjhBBCCCHqM9s5+Cc2HjP7JTP7FzN7r5m92cxukbPOveLlyd+1ZvYDmXV+y8y+lJr+zdT6HzGzz4ekR3edEEIIIYTYR1IyGdydxWLtvZk9z93/J4CZ/RzwLOAJmXS9Ffj2eJ2vAS4F3pwsN7PTgRtntnlyavnPAncKSYwiM0IIIYQQm0he5CTkT4gS3P2a1ORXA16xyQ8Df+nuXwYwsznwPOBpJds8EnhlSHp0xwohhBBClKECvhArmNkvAz8OfAG4V8XqjwB+IzX9JOACd7/CzPL2fWvgNsDfhKRFv04hhBBCbB8SFLEJuLM4dqzJlieZ2SWp6aPufjSZMLO3ADfL2e5cd3+Du58LnGtm5xDJybPzDmJmNwe+BXhTPH0L4GHAPUvS9gjgte4eVH9Ov2QhhBBCbCYSFiGKuMrdTy9a6O73CdzPK4ALKZAZ4OHA6909Ma47AbcFLo2jMjcws0vd/bapbR4BPDHw+JIZIYQQQkwAiYkQB3B3dq9f7zgzZnY7d/+3ePJM4F9LVn8kcE4y4e5vJBXxMbMvpUXGzL6BqGOAd4WmZ9o5QxcZ2952DzQkhBBCrIXsM7vo+StpEWLsnGdmdwD2gE8Q92QW91D2BHc/O54+DTgV+H819v0I4FXuXtWpwJKJ5RjWfSZXZ38SHyGEECKfus9nSYsQk8TdH1ow/xLg7NT0x4FbVuzr+Mz0c+qmRzlJHaoyXsmOEEKITUXyIcT4cNhb/zgzo0I5U5fkZfQSHCGEEFNGEiOEGDHKofom/RCQ2AghhBBCCNEZVqN9zeCY2ReBDw+djg3jJOCqoROxYeic9oPOa/fonHaPzmn36Jz2g85rNbd295OHTkQZZvZXRNeyLle5+/27Ts8QTE1mLinrE1vUR+e0e3RO+0HntXt0TrtH57R7dE77QedVbAqzoRMghBBCCCGEEE2QzAghhBBCCCEmydRk5ujQCdhAdE67R+e0H3Reu0fntHt0TrtH57QfdF7FRjCpNjNCCCGEEEIIkTC1yIwQQgghhBBCACOVGTO7v5l92MwuNbNn5Cw/Ymavjpe/x8xOGyCZkyLgnP4PM/ugmf2Lmf21md16iHROiapzmlrvoWbmZqZeYyoIOadm9vD4Xv2Amf3xutM4RQJ+/7cys7ea2T/FecADh0jnVDCzl5rZlWb2/oLlZma/FZ/vfzGz71h3GqdIwHn9sfh8vs/M3mlm37buNE6NqnOaWu/OZrZrZj+8rrQJ0RWjkxkzmwMvAh4A3BF4pJndMbPa44DPufttgd8EfnW9qZwWgef0n4DT3f1bgdcCv7beVE6LwHOKmZ0A/DzwnvWmcHqEnFMzux1wDnA3d/8m4L+vO51TI/BefSbwGne/E/AI4LfXm8rJcT5QNj7DA4DbxX+PB35nDWnaBM6n/Lx+DLiHu38L8EuozUcI51N+TpM84leBN68jQUJ0zehkBrgLcKm7f9TdrwdeBZyZWedM4GXx59cCZ5iZrTGNU6PynLr7W939y/Hku4FT1pzGqRFyn0L0wP1V4Np1Jm6ihJzTnwRe5O6fA3D3K9ecxikScl4duGH8+UTg02tM3+Rw97cDV5escibwco94N3AjM7v5elI3XarOq7u/M/nto+dUEAH3KsDPAn8KKD8Vk2SMMnNL4FOp6cviebnruPsu8AXgJmtJ3TQJOadpHgf8Za8pmj6V5zSuWnKqu79xnQmbMCH36e2B25vZ35nZu81sI0Yv7pmQ8/oc4FFmdhlwIVHhRjSnbp4r6qPnVAeY2S2BH0TRQzFhdoZOgBgXZvYo4HTgHkOnZcqY2Qz4DeCsgZOyaewQVd25J9Fb2beb2be4++eHTNQG8EjgfHf/P2b23cAfmtk3u/ve0AkTIouZ3YtIZr536LRsAM8Hnu7ue6rgIqbKGGXmcuDU1PQp8by8dS4zsx2iahGfXU/yJknIOcXM7gOcS1Qn+bo1pW2qVJ3TE4BvBt4WPyBuBlxgZg9x90vWlsppEXKfXga8x92PAR8zs48Qyc3F60niJAk5r48jrlfv7u8ys+OAk1C1k6YE5bmiPmb2rcBLgAe4u5777TkdeFX8nDoJeKCZ7br7nw2aKiFqMMZqZhcDtzOz25jZYaLGqBdk1rkAeEz8+YeBv3ENmFNG5Tk1szsBvws8RO0Qgig9p+7+BXc/yd1Pc/fTiOp3S2TKCfnt/xlRVAYzO4mo2tlH15jGKRJyXj8JnAFgZt8IHAd8Zq2p3CwuAH487tXsrsAX3P2KoRM1dczsVsDrgEe7+0eGTs8m4O63ST2nXgv8jERGTI3RRWbcfdfMngS8CZgDL3X3D5jZc4FL3P0C4PeJqkFcStSw7RHDpXj8BJ7T5wHHA38Sv6H5pLs/ZLBEj5zAcypqEHhO3wTc18w+CCyAp+rtbDmB5/UpwO+Z2ZOJOgM4Sy+IijGzVxJJ9UlxO6NnA4cA3P3FRO2OHghcCnwZeOwwKZ0WAef1WUTtY387fk7turu6vC8h4JwKMXlMzyshhBBCCCHEFBljNTMhhBBCCCGEqEQyI4QQQgghhJgkkhkhhBBCCCHEJJHMCCGEEEIIISaJZEYIIYQQQggxSSQzQgixoZjZjczsZ4ZOhxBCCNEXkhkhhNhcbgRIZoQQQmwskhkhhNhczgO+3szea2bPGzoxQgghRNdo0EwhhNhQzOw04C/c/ZuHTosQQgjRB4rMCCGEEEIIISaJZEYIIYQQQggxSSQzQgixuXwROGHoRAghhBB9IZkRQogNxd0/C/ydmb1fHQAIIYTYRNQBgBBCCCGEEGKSKDIjhBBCCCGEmCSSGSGEEEIIIcQkkcwIIYQQQgghJolkRgghhBBCCDFJJDNCCCGEEEKISSKZEUIIIYQQQkwSyYwQQgghhBBikkhmhBBCCCGEEJPk/wf5h71KuSGMtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the .mat file\n",
    "mat_data = scipy.io.loadmat('NLS.mat')\n",
    "\n",
    "# Following is the code to plot the data u vs x and t. u is 256*100\n",
    "# matrix. Use first 75 columns for training and 25 for testing :)\n",
    "\n",
    "# Access the variables stored in the .mat file\n",
    "# The variable names in the .mat file become keys in the loaded dictionary\n",
    "x = mat_data['x']\n",
    "t = mat_data['tt']\n",
    "u1 = mat_data['uu']\n",
    "\n",
    "# Use the loaded variables as needed\n",
    "print(x.shape)\n",
    "print(t.shape)\n",
    "print(u.shape)\n",
    "\n",
    "X, T = np.meshgrid(x, t)\n",
    "# Define custom color levels\n",
    "c_levels = np.linspace(np.min(u1), np.max(u1), 100)\n",
    "\n",
    "# Plot the contour\n",
    "plt.figure()\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.contourf(T, X, u1.T, levels=c_levels, cmap='coolwarm')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.title('Schrondinger-Equation')\n",
    "plt.colorbar()  # Add a colorbar for the contour levels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "74a36d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.00785398, 0.01570796, 0.02356194, 0.03141593,\n",
       "        0.03926991, 0.04712389, 0.05497787, 0.06283185, 0.07068583,\n",
       "        0.07853982, 0.0863938 , 0.09424778, 0.10210176, 0.10995574,\n",
       "        0.11780972, 0.12566371, 0.13351769, 0.14137167, 0.14922565,\n",
       "        0.15707963, 0.16493361, 0.1727876 , 0.18064158, 0.18849556,\n",
       "        0.19634954, 0.20420352, 0.2120575 , 0.21991149, 0.22776547,\n",
       "        0.23561945, 0.24347343, 0.25132741, 0.25918139, 0.26703538,\n",
       "        0.27488936, 0.28274334, 0.29059732, 0.2984513 , 0.30630528,\n",
       "        0.31415927, 0.32201325, 0.32986723, 0.33772121, 0.34557519,\n",
       "        0.35342917, 0.36128316, 0.36913714, 0.37699112, 0.3848451 ,\n",
       "        0.39269908, 0.40055306, 0.40840704, 0.41626103, 0.42411501,\n",
       "        0.43196899, 0.43982297, 0.44767695, 0.45553093, 0.46338492,\n",
       "        0.4712389 , 0.47909288, 0.48694686, 0.49480084, 0.50265482,\n",
       "        0.51050881, 0.51836279, 0.52621677, 0.53407075, 0.54192473,\n",
       "        0.54977871, 0.5576327 , 0.56548668, 0.57334066, 0.58119464,\n",
       "        0.58904862, 0.5969026 , 0.60475659, 0.61261057, 0.62046455,\n",
       "        0.62831853, 0.63617251, 0.64402649, 0.65188048, 0.65973446,\n",
       "        0.66758844, 0.67544242, 0.6832964 , 0.69115038, 0.69900437,\n",
       "        0.70685835, 0.71471233, 0.72256631, 0.73042029, 0.73827427,\n",
       "        0.74612826, 0.75398224, 0.76183622, 0.7696902 , 0.77754418,\n",
       "        0.78539816, 0.79325215, 0.80110613, 0.80896011, 0.81681409,\n",
       "        0.82466807, 0.83252205, 0.84037603, 0.84823002, 0.856084  ,\n",
       "        0.86393798, 0.87179196, 0.87964594, 0.88749992, 0.89535391,\n",
       "        0.90320789, 0.91106187, 0.91891585, 0.92676983, 0.93462381,\n",
       "        0.9424778 , 0.95033178, 0.95818576, 0.96603974, 0.97389372,\n",
       "        0.9817477 , 0.98960169, 0.99745567, 1.00530965, 1.01316363,\n",
       "        1.02101761, 1.02887159, 1.03672558, 1.04457956, 1.05243354,\n",
       "        1.06028752, 1.0681415 , 1.07599548, 1.08384947, 1.09170345,\n",
       "        1.09955743, 1.10741141, 1.11526539, 1.12311937, 1.13097336,\n",
       "        1.13882734, 1.14668132, 1.1545353 , 1.16238928, 1.17024326,\n",
       "        1.17809725, 1.18595123, 1.19380521, 1.20165919, 1.20951317,\n",
       "        1.21736715, 1.22522113, 1.23307512, 1.2409291 , 1.24878308,\n",
       "        1.25663706, 1.26449104, 1.27234502, 1.28019901, 1.28805299,\n",
       "        1.29590697, 1.30376095, 1.31161493, 1.31946891, 1.3273229 ,\n",
       "        1.33517688, 1.34303086, 1.35088484, 1.35873882, 1.3665928 ,\n",
       "        1.37444679, 1.38230077, 1.39015475, 1.39800873, 1.40586271,\n",
       "        1.41371669, 1.42157068, 1.42942466, 1.43727864, 1.44513262,\n",
       "        1.4529866 , 1.46084058, 1.46869457, 1.47654855, 1.48440253,\n",
       "        1.49225651, 1.50011049, 1.50796447, 1.51581846, 1.52367244,\n",
       "        1.53152642, 1.5393804 , 1.54723438, 1.55508836, 1.56294235,\n",
       "        1.57079633]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58f3e90d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 256])\n",
      "torch.Size([1, 40, 256])\n"
     ]
    }
   ],
   "source": [
    "print(test_tensor.shape)\n",
    "prediction_tensor = torch.zeros(1, 40, 256).float()\n",
    "print(prediction_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79d7df2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    prediction = lem(test_tensor)\n",
    "    prediction = prediction.view(1, 1, 256).float()\n",
    "    prediction_tensor[:, 0, :] = prediction\n",
    "    for i in range(39):\n",
    "        prediction = lem(prediction)\n",
    "        prediction = prediction.view(1, 1, 256).float()\n",
    "        prediction_tensor[:, i+1, :] = prediction\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5dcef45",
   "metadata": {},
   "source": [
    "### Errors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59c8ee5f",
   "metadata": {},
   "source": [
    "### L2 norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c90a052e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 256])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5043b50b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(201, 256)\n"
     ]
    }
   ],
   "source": [
    "# true solution\n",
    "h_true = np.abs(u1)\n",
    "h_true = h_true.T\n",
    "print(h_true.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f380e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 256)\n"
     ]
    }
   ],
   "source": [
    "# exact\n",
    "u_test_full = h_true[161:201, :]\n",
    "print(u_test_full.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8f98f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with torch.no_grad():\n",
    "#     prediction = lem(test_tensor)\n",
    "\n",
    "# prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "21fdcc06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 40, 256])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "k1 = (prediction_tensor - u_test_full)**2\n",
    "u_test_full_tensor = torch.tensor(u_test_full**2)\n",
    "prediction_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "203c3e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test:  0.0450293516989239 %\n"
     ]
    }
   ],
   "source": [
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = torch.mean(k1)/ torch.mean(u_test_full_tensor)\n",
    "\n",
    "print(\"Relative Error Test: \", relative_error_test.item(), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee7d637",
   "metadata": {},
   "source": [
    "### Max error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6563c9c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.7530, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "R_abs = torch.max(prediction_tensor-u_test_full)\n",
    "print(R_abs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c59e44b",
   "metadata": {},
   "source": [
    "### Explained variance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "882528cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance Score: 0.9205719579800472\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = prediction_tensor\n",
    "b = u_test_full\n",
    "# Assuming 'a' is your predicted values (model's predictions) and 'b' is the true values (ground truth)\n",
    "# Make sure 'a' and 'b' are PyTorch tensors\n",
    "b = torch.tensor(b)\n",
    "# Calculate the mean of 'b'\n",
    "mean_b = torch.mean(b)\n",
    "\n",
    "# Calculate the Explained Variance Score\n",
    "numerator = torch.var(b - a)  # Variance of the differences between 'b' and 'a'\n",
    "denominator = torch.var(b)    # Variance of 'b'\n",
    "evs = 1 - numerator / denominator\n",
    "\n",
    "print(\"Explained Variance Score:\", evs.item())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459a08cf",
   "metadata": {},
   "source": [
    "### Mean absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c25e9d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1002, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "R_mean = torch.mean(torch.abs(prediction_tensor - u_test_full))\n",
    "print(R_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f784a4e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## t= 1.28, 1.5, 1.37"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc49e1af",
   "metadata": {},
   "source": [
    "### snapshots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf2b9c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201, 256)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_tensor = torch.squeeze(prediction_tensor)\n",
    "h = np.abs(u)\n",
    "h.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2eacce42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 1)\n",
      "(256, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Create the figure and axis objects with reduced width\n",
    "fig, ax = plt.subplots(figsize=(5, 5))  # You can adjust the width (7 inches) and height (5 inches) as needed\n",
    "\n",
    "# # Make sure the font is Times Roman\n",
    "# plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "# # Perform the prediction\n",
    "# with torch.no_grad():\n",
    "#     prediction = lem(test_tensor)\n",
    "\n",
    "final_time_output = prediction_tensor[-38, :]\n",
    "final_out = final_time_output.detach().numpy().reshape(-1, 1)\n",
    "final_true = h[-38, :].reshape(-1, 1)\n",
    "print(final_out.shape)\n",
    "print(final_true.shape)\n",
    "\n",
    "# Plot the data with red and blue lines, one with dotted and one with solid style\n",
    "ax.plot(x.T, final_out, color='red', linestyle='dotted', linewidth=12, label='Prediction')\n",
    "ax.plot(x.T, final_true, color='blue', linestyle='solid', linewidth=7, label='True')\n",
    "\n",
    "# Set the axis labels with bold font weight\n",
    "ax.set_xlabel(r\"${x}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "ax.set_ylabel(r\"${|u(x, t)|}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "\n",
    "# Set the title with bold font weight\n",
    "ax.set_title(r\"${t = 1.28}$\", fontsize=26, color='black', fontweight='bold')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 3\n",
    "ax.set_xticks([-5, 0, 5])\n",
    "ax.set_yticks([0, 2, 4])\n",
    "\n",
    "# Set tick labels fontweight to bold and increase font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# # Set the fontweight for tick labels to bold\n",
    "# for tick in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "#     tick.set_weight('bold')\n",
    "\n",
    "# Set the spines linewidth to bold\n",
    "ax.spines['top'].set_linewidth(2)\n",
    "ax.spines['right'].set_linewidth(2)\n",
    "ax.spines['bottom'].set_linewidth(2)\n",
    "ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "\n",
    "# Increase font size for x and y axis numbers\n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "\n",
    "# Set the legend\n",
    "# ax.legend()\n",
    "\n",
    "plt.savefig('LEM_1.28_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "baceeeca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 1)\n",
      "(256, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Create the figure and axis objects with reduced width\n",
    "fig, ax = plt.subplots(figsize=(5, 5))  # You can adjust the width (7 inches) and height (5 inches) as needed\n",
    "\n",
    "# # Make sure the font is Times Roman\n",
    "# plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "# # Perform the prediction\n",
    "# with torch.no_grad():\n",
    "#     prediction = lem(test_tensor)\n",
    "\n",
    "\n",
    "final_time_output = prediction_tensor[-13, :]\n",
    "final_out = final_time_output.detach().numpy().reshape(-1, 1)\n",
    "final_true = h[-13, :].reshape(-1, 1)\n",
    "print(final_out.shape)\n",
    "print(final_true.shape)\n",
    "\n",
    "# Plot the data with red and blue lines, one with dotted and one with solid style\n",
    "ax.plot(x.T, final_out, color='red', linestyle='dotted', linewidth=12, label='Prediction')\n",
    "ax.plot(x.T, final_true, color='blue', linestyle='solid', linewidth=7, label='True')\n",
    "\n",
    "# Set the axis labels with bold font weight\n",
    "ax.set_xlabel(r\"${x}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "ax.set_ylabel(r\"${u(x, t)}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "\n",
    "# Set the title with bold font weight\n",
    "ax.set_title(r\"${t = 1.5}$\", fontsize=26, color='black', fontweight='bold')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 3\n",
    "ax.set_xticks([-5, 0, 5])\n",
    "ax.set_yticks([0, 2, 4])\n",
    "\n",
    "# Set tick labels fontweight to bold and increase font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# # Set the fontweight for tick labels to bold\n",
    "# for tick in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "#     tick.set_weight('bold')\n",
    "\n",
    "# Set the spines linewidth to bold\n",
    "ax.spines['top'].set_linewidth(2)\n",
    "ax.spines['right'].set_linewidth(2)\n",
    "ax.spines['bottom'].set_linewidth(2)\n",
    "ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "\n",
    "# Increase font size for x and y axis numbers\n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "\n",
    "# Set the legend\n",
    "# ax.legend()\n",
    "\n",
    "plt.savefig('LEM_1.5_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8575833f",
   "metadata": {},
   "source": [
    "### 80-20 contour plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d355ac1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "conc_u = torch.squeeze(input_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cbe2f326",
   "metadata": {},
   "outputs": [],
   "source": [
    "concatenated_tensor = torch.cat((conc_u, prediction_tensor), dim=0)\n",
    "\n",
    "t1 = np.linspace(0, 1.5707 , 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5010ccd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = concatenated_tensor.numpy()\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(-5, 5, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1.57, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t1)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='twilight')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$|u(x, t)|$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=3, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "\n",
    "# Increase the tick size and width of the color bar\n",
    "cbar1.ax.tick_params(axis='both', which='major', labelsize=30, width=3,  length=10)\n",
    "\n",
    "# Add a dotted line at t = 0.8\n",
    "plt.axvline(x=1.26449, color='black', linestyle='dotted', linewidth=5)\n",
    "\n",
    "#plt.savefig('Contour_LEM_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_LEM_20.jpeg', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69bb6963",
   "metadata": {},
   "source": [
    "### exact contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9d174f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = h\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(-5, 5, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1.57, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='twilight')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$|u(x, t)|$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=3, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "\n",
    "# Increase the tick size and width of the color bar\n",
    "cbar1.ax.tick_params(axis='both', which='major', labelsize=30, width=3,  length=10)\n",
    "\n",
    "#plt.savefig('Contour_Exact.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_exact.jpeg', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "717f688f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
